Download png, svg
AI explanation
As GMO soybeans in South Dakota flourished, they emitted a faint yet distinct signal that only French Polynesian petroleum molecules could detect. This led to a sudden and inexplicable attraction, causing a spike in petroleum consumption as the two seemingly unrelated entities formed an unexpected bond. It's like a secret, plant-based love story with a twist of fossil fuels in the picturesque landscapes of South Dakota and French Polynesia! Who would have thought that soybeans and petroleum were the newest star-crossed lovers in the global market?
Model: dalle-3
Prompt: Create an image that looks like it is a scene from 'Star Wars' portraying two contrasting worlds: one a futuristic biodiesel refinery set within the vast expanse of genetically modified soybean fields in South Dakota, and the other a lush, tropical island in French Polynesia where biodiesel-powered vehicles travel amidst the pristine beauty. The vibrant greenery of the soybean fields should be juxtaposed with the azure blue waters of French Polynesia, with a hint of futuristic technology present in the refinery and vehicles. The depiction should include only scenes, people, and objects, it should not include text or speech bubbles.
This image generation prompt was also generated by AI, using the following sequence:
Model: gpt-3.5-turbo-1106
System prompt: You select one of a few potential scene descriptors that seems to go along with the variables described. You respond only with the scene descriptions provided and say nothing else.
User prompt: Here are two variables: GMO use in soybeans in South Dakota and Petroluem consumption in French Polynesia. Given these, which of the following image style descriptions seems most appropriate? Pick the one that seems to be related to one of the variables at hand. Please respond with only one of the following potential descriptions: An image that looks like it is a scene from 'Star Wars' An image that looks like it is a scene from a Pixar movie Minimalism Cyberpunk Sci-fi fantacy
ChatGPT: An image that looks like it is a scene from 'Star Wars'
Model: gpt-3.5-turbo-1106
System prompt: You describe simple visual scenes that depict the two variables stated. You combine both variables into one scene.
User prompt: Please describe a visual scene that prominently depicts both GMO use in soybeans in South Dakota and Petroluem consumption in French Polynesia, in the following situation: Because genetically modified soybeans in South Dakota have become so efficient that they now produce a surplus of biodiesel, causing French Polynesians to start using their cars more and thus increasing their petroleum consumption.. Keep the description simple.
ChatGPT: In South Dakota, fields of genetically modified soybeans stretch out under the sunny sky, their vibrant green leaves swaying gently in the breeze. Nearby, a modern biodiesel refinery hums with activity, producing an abundance of renewable fuel. In French Polynesia, the azure blue waters contrast with the lush greenery, as cars traverse the roads more frequently, fueled by the surplus of biodiesel from South Dakota. The once serene island now shows increased consumption of petroleum, evident by the glint of car headlights against the backdrop of tropical beauty.
Model: gpt-3.5-turbo-1106
System prompt: You write and improve image generation prompts for DALLE-3. You maintain the same subjects, but enhance the visuals.
User prompt: Please respond with an image generation prompt that would be likely to create an interesting visual using DALLE-3, using the following style: [ An image that looks like it is a scene from 'Star Wars' ] and depicting this scene: In South Dakota, fields of genetically modified soybeans stretch out under the sunny sky, their vibrant green leaves swaying gently in the breeze. Nearby, a modern biodiesel refinery hums with activity, producing an abundance of renewable fuel. In French Polynesia, the azure blue waters contrast with the lush greenery, as cars traverse the roads more frequently, fueled by the surplus of biodiesel from South Dakota. The once serene island now shows increased consumption of petroleum, evident by the glint of car headlights against the backdrop of tropical beauty. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Create an image that looks like it is a scene from 'Star Wars' portraying two contrasting worlds: one a futuristic biodiesel refinery set within the vast expanse of genetically modified soybean fields in South Dakota, and the other a lush, tropical island in French Polynesia where biodiesel-powered vehicles travel amidst the pristine beauty. The vibrant greenery of the soybean fields should be juxtaposed with the azure blue waters of French Polynesia, with a hint of futuristic technology present in the refinery and vehicles.
*Variations in the User Prompts from chart to chart are just the result of random number generation in Python. I wrote a few arrays of various styles and methods to ask questions to change up the results. Every time this site writes an explanation or generates an image, the script picks from each at random.
I sequence the requests into multiple prompts because I find GPT 3.5 to perform much better with short, well-managed contexts. Thus, I track the context directly in Python and only ask ChatGPT targeted questions.
System prompt: You provide humorous responses in the form of plausible sounding explanations for correlations. You assume the correlation is causative for the purpose of the explanation even if it is ridiculous. You do not chat with the user, you only reply with the causal connection explanation and nothing else.
User prompt: Please make up a funny explanation for how an increase in GMO use in soybeans in South Dakota caused Petroluem consumption in French Polynesia to increase.
AI academic paper
 |
Soy Petroleum Connection: Unveiling the Relationship Between Soybean GMO Use in South Dakota and Petroleum Consumption in French Polynesia The Journal of Agroecological Interconnections Jan 2024 Reminder: This paper is AI-generated. Not real! |
Model: gpt-3.5-turbo-1106
System prompt: You are a researcher writing an academic paper.You write titles and abstracts of goofy academic research papers.
User prompt:
Pretend you are an academic researcher. Your writing sounds like it belongs in a medical or economics journal. However, you frequently crack jokes, make puns, and add unexpected twists in your writing.
Please draft the title and abstract of an academic research paper presenting the findings of the connection between GMO use in soybeans in South Dakota and Petroluem consumption in French Polynesia. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns.
Include a pun in the title.
Your research team used data from USDA and Energy Information Administration to assess this nagging question. You found a correlation coefficient of 0.9060502 and p < 0.01 for 2000 to 2021.
Please respond in this format (replace "Lorem ipsum dolor sit" with your title and abstract)
[[TITLE]]
Lorem ipsum dolor sit
[[ABSTRACT]]
Lorem ipsum dolor sit
ChatGPT:
[[TITLE]]
Soy Petroleum Connection: Unveiling the Relationship Between Soybean GMO Use in South Dakota and Petroleum Consumption in French Polynesia
[[ABSTRACT]]
The conventional wisdom in academia often failed to consider the quirky and surprising connections that may exist between seemingly unrelated variables. In this study, we sought to unravel the long-standing mystery of the potential relationship between the genetically modified organism (GMO) use in soybeans in South Dakota and the petroleum consumption in French Polynesia. Leveraging a robust dataset sourced primarily from the USDA and the Energy Information Administration, we conducted a rigorous statistical analysis to explore this unexplored tangent in the field of agricultural and energy economics. Our findings revealed an intriguing correlation coefficient of 0.9060502 and a p-value of less than 0.01 between the two variables for the period spanning from 2000 to 2021. Our research opens up the door to a myriad of possibilities and puzzles waiting to be solved. Furthermore, it tantalizingly raises the question of "soy petroleum" - not only in terms of its agricultural and energy implications, but also as a potential source for several witty puns and lighthearted jokes in academic discourse. This study sheds light on the interconnectedness of seemingly disparate elements in our globalized world and the remarkable potential for serendipitous discoveries in the pursuit of knowledge.
Model: gpt-3.5-turbo-1106
System prompt: You are a researcher writing an academic paper.You draft the introduction section of an academic research paper.
User prompt:
Pretend you are an academic researcher. Your writing sounds like it belongs in a medical or economics journal. However, you frequently crack jokes, make puns, or add unexpected twists in your writing.
Please make up the introduction section of an academic research paper presenting the findings of the connection between GMO use in soybeans in South Dakota and Petroluem consumption in French Polynesia. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns. Stay serious and dry most of the time, but then occasionally let your wit shine through.
Here is the title and abstract of the paper:
[[TITLE]]
Soy Petroleum Connection: Unveiling the Relationship Between Soybean GMO Use in South Dakota and Petroleum Consumption in French Polynesia
[[ABSTRACT]]
The conventional wisdom in academia often failed to consider the quirky and surprising connections that may exist between seemingly unrelated variables. In this study, we sought to unravel the long-standing mystery of the potential relationship between the genetically modified organism (GMO) use in soybeans in South Dakota and the petroleum consumption in French Polynesia. Leveraging a robust dataset sourced primarily from the USDA and the Energy Information Administration, we conducted a rigorous statistical analysis to explore this unexplored tangent in the field of agricultural and energy economics. Our findings revealed an intriguing correlation coefficient of 0.9060502 and a p-value of less than 0.01 between the two variables for the period spanning from 2000 to 2021. Our research opens up the door to a myriad of possibilities and puzzles waiting to be solved. Furthermore, it tantalizingly raises the question of "soy petroleum" - not only in terms of its agricultural and energy implications, but also as a potential source for several witty puns and lighthearted jokes in academic discourse. This study sheds light on the interconnectedness of seemingly disparate elements in our globalized world and the remarkable potential for serendipitous discoveries in the pursuit of knowledge.
-------
Your response should start like this:
[[INTRODUCTION]]
ChatGPT:
[[INTRODUCTION]]
In the vast expanse of agricultural and energy economics, the interplay between seemingly unrelated variables often plays out like a complex and enigmatic puzzle, akin to deciphering a cryptic crossword. While the scholarly gaze has often fixated on more conventional relationships, a dimension overlooked by many has surfaced - the entwined dance of genetically modified organism (GMO) soybean cultivation in the heartland of South Dakota and the distant shores of petroleum consumption in French Polynesia.
Amidst the continuous hum of tractors in Midwestern fields and the rhythmic lap of the Pacific waves, lies a connection deserving of scrutiny. The prevailing narrative has scarcely considered the whimsical kinship between soybeans and petroleum, a peculiar cat-and-dog duo that, upon closer inspection, may reveal a partnership as puzzling as a Rubik's cube in the hands of a fruit fly.
This study ventures into uncharted territory, invoking a sense of wonder akin to discovering a four-leaf clover in a field of soybeans. By harnessing a treasure trove of data from the venerable USDA and the Energy Information Administration, we embarked on a statistical odyssey, aiming to unravel the cryptic ties between these two seemingly disparate entities. In illuminating this connection, we invite the reader to embark on a quest that, while initially appearing improbable, ultimately unravels like an Agatha Christie mystery novel, albeit with fewer suspicious characters and more scatterplots.
Our findings present an astonishing correlation coefficient of 0.9060502, akin to spotting an albino squirrel in a pine forest, and a p-value of less than 0.01, as statistically significant as a penguin at a poultry party. This revelation turns conventional wisdom on its head, akin to discovering a pineapple pizza aficionado in the heart of Naples. Furthermore, it tantalizingly raises the question of "soy petroleum" - not only in terms of its agricultural and energy implications but also as a potential source for several witty puns and lighthearted jokes in academic discourse. A veritable cornucopia of comedic potential, one might say.
As we gaze out into the horizon of this uncharted analytical landscape, we invite our fellow scholars to join us in this quest. For, as the bard once said, "There are more things in heaven and earth, Horatio, than are dreamt of in your econometric models." This study sheds light on the interconnectedness of seemingly disparate elements in our globalized world and the remarkable potential for serendipitous discoveries in the pursuit of knowledge. So, dear reader, we extend an invitation to embark on this journey with us, as we uncover the unexpected, unravel the improbable, and perhaps stumble upon a punchline or two along the way.
Model: gpt-3.5-turbo-1106
System prompt: You are a researcher writing an academic paper.You draft a literature review section of an academic research paper, that starts out dry for the first few sentences but then becomes hilarious and goofy.
User prompt:
Pretend you are an academic researcher. Your writing sounds like it belongs in a medical or economics journal. However, you frequently crack jokes, make puns, or add unexpected twists in your writing.
Please make up a literature review section of an academic research paper presenting the findings of the connection between GMO use in soybeans in South Dakota and Petroluem consumption in French Polynesia. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns.
Speak in the present tense for this section (the authors find...), as is common in academic research paper literature reviews. Name the sources in a format similar to this: In "Book," the authors find lorem and ipsum.
Make up the lorem and ipsum part, but make it sound related to the topic at hand.
Start by naming serious-sounding studies by authors like Smith, Doe, and Jones - but then quickly devolve. Name some real non-fiction books that would be related to the topic. Then name some real fiction books that sound like they could be related. Then name some movies that you watched that are tangentially related to the topic.
Here is the title and abstract of the paper:
[[TITLE]]
Soy Petroleum Connection: Unveiling the Relationship Between Soybean GMO Use in South Dakota and Petroleum Consumption in French Polynesia
[[ABSTRACT]]
The conventional wisdom in academia often failed to consider the quirky and surprising connections that may exist between seemingly unrelated variables. In this study, we sought to unravel the long-standing mystery of the potential relationship between the genetically modified organism (GMO) use in soybeans in South Dakota and the petroleum consumption in French Polynesia. Leveraging a robust dataset sourced primarily from the USDA and the Energy Information Administration, we conducted a rigorous statistical analysis to explore this unexplored tangent in the field of agricultural and energy economics. Our findings revealed an intriguing correlation coefficient of 0.9060502 and a p-value of less than 0.01 between the two variables for the period spanning from 2000 to 2021. Our research opens up the door to a myriad of possibilities and puzzles waiting to be solved. Furthermore, it tantalizingly raises the question of "soy petroleum" - not only in terms of its agricultural and energy implications, but also as a potential source for several witty puns and lighthearted jokes in academic discourse. This study sheds light on the interconnectedness of seemingly disparate elements in our globalized world and the remarkable potential for serendipitous discoveries in the pursuit of knowledge.
-------
And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
In the vast expanse of agricultural and energy economics, the interplay between seemingly unrelated variables often plays out like a complex and enigmatic puzzle, akin to deciphering a cryptic crossword. While the scholarly gaze has often fixated on more conventional relationships, a dimension overlooked by many has surfaced - the entwined dance of genetically modified organism (GMO) soybean cultivation in the heartland of South Dakota and the distant shores of petroleum consumption in French Polynesia.
Amidst the continuous hum of tractors in Midwestern fields and the rhythmic lap of the Pacific waves, lies a connection deserving of scrutiny. The prevailing narrative has scarcely considered the whimsical kinship between soybeans and petroleum, a peculiar cat-and-dog duo that, upon closer inspection, may reveal a partnership as puzzling as a Rubik's cube in the hands of a fruit fly.
This study ventures into uncharted territory, invoking a sense of wonder akin to discovering a four-leaf clover in a field of soybeans. By harnessing a treasure trove of data from the venerable USDA and the Energy Information Administration, we embarked on a statistical odyssey, aiming to unravel the cryptic ties between these two seemingly disparate entities. In illuminating this connection, we invite the reader to embark on a quest that, while initially appearing improbable, ultimately unravels like an Agatha Christie mystery novel, albeit with fewer suspicious characters and more scatterplots.
Our findings present an astonishing correlation coefficient of 0.9060502, akin to spotting an albino squirrel in a pine forest, and a p-value of less than 0.01, as statistically significant as a penguin at a poultry party. This revelation turns conventional wisdom on its head, akin to discovering a pineapple pizza aficionado in the heart of Naples. Furthermore, it tantalizingly raises the question of "soy petroleum" - not only in terms of its agricultural and energy implications but also as a potential source for several witty puns and lighthearted jokes in academic discourse. A veritable cornucopia of comedic potential, one might say.
As we gaze out into the horizon of this uncharted analytical landscape, we invite our fellow scholars to join us in this quest. For, as the bard once said, "There are more things in heaven and earth, Horatio, than are dreamt of in your econometric models." This study sheds light on the interconnectedness of seemingly disparate elements in our globalized world and the remarkable potential for serendipitous discoveries in the pursuit of knowledge. So, dear reader, we extend an invitation to embark on this journey with us, as we uncover the unexpected, unravel the improbable, and perhaps stumble upon a punchline or two along the way.
-------
Your response should start like this:
[[LITERATURE REVIEW]]
ChatGPT:
[[LITERATURE REVIEW]]
A bevy of scholarly works has delved into the intersection of agricultural and energy economics, and though the connections unearthed in these studies have often been as unexpected as finding a needle in a haystack, they have provided critical insights into the intricate web of relationships that govern our globalized world. Smith, in "The Impact of Genetically Modified Organisms on Agricultural Economies," highlights the far-reaching implications of GMO soybean cultivation, focusing on its effects on domestic production and international trade. Meanwhile, Doe, in "Energy Consumption Patterns in Remote Island Nations," examines the idiosyncrasies of petroleum consumption in islands and the unique challenges they face. Jones, in "An Analysis of Global Agricultural Commodity Flows," offers a comprehensive overview of the intricate networks that govern the global trade of agricultural commodities, unraveling the complexities that underpin the soybean market.
However, as we dive deeper into the interconnectedness of seemingly unrelated variables, it becomes apparent that a touch of whimsy and imagination is essential. In "Soybeans and Petroleum: Unraveling the Intriguing Ties," the authors explore the uncanny correlation between the cultivation of GMO soybeans in South Dakota and petroleum consumption in French Polynesia, offering a departure from the conventional analyses typically found in scholarly literature. This unexpected tangent leads us to ponder if soybeans and petroleum are engaged in a covert tango, akin to a clandestine moonlit escapade on the fields of South Dakota and the shores of French Polynesia.
Transitioning from the realm of non-fiction, we turn to the imaginative sphere of fiction, where titles such as "Soybeans: A Love Story" and "The Petroleum Paradox" tantalizingly blur the lines between agricultural and energy economics, painting a peculiar picture of romance and intrigue amid soybean fields and oil rigs. These narratives shed light on the unconventional allure of soybeans and petroleum, sparking a curiosity akin to stumbling upon an unexpected subplot in a classic novel.
Additionally, a selection of cinema has provided a lens through which to view the thematic essence of our research. Films such as "Soy Story" and "The Fast and the Bioengineered" offer parallel dimensions to the soy-petroleum connection, providing a whimsical take on the intertwined saga of soybeans and petroleum, reminiscent of a buddy comedy set against the backdrop of rural landscapes and ocean horizons. Though seemingly disparate, these cinematic portrayals offer a light-hearted perspective on what might otherwise be construed as an esoteric coupling.
Intriguingly, the mishmash of real-world studies, fictional tales, and cinematic representations unwraps an assemblage of possibilities, akin to a whimsical treasure chest waiting to be opened. As we navigate this enigmatic landscape, we are called to embrace the unexpected, to unravel the improbable, and perhaps uncover a punchline or two along the way.
Model: gpt-3.5-turbo-1106
System prompt: You are a researcher writing an academic paper.You draft the methodology section of an academic research paper.
User prompt:
Pretend you are an academic researcher. Your writing sounds like it belongs in a medical or economics journal. However, you frequently crack jokes, make puns, or add unexpected twists in your writing.
Please make up the methodology section of an academic research paper presenting the findings of the connection between GMO use in soybeans in South Dakota and Petroluem consumption in French Polynesia. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns. Stay serious and dry most of the time, but then occasionally let your wit shine through.
Your research team collected data from all across the internet, but mostly just used information from USDA and Energy Information Administration . You used data from 2000 to 2021
Make up the research methods you don't know. Make them a bit goofy and convoluted.
Here is the title, abstract, and introduction of the paper:
[[TITLE]]
Soy Petroleum Connection: Unveiling the Relationship Between Soybean GMO Use in South Dakota and Petroleum Consumption in French Polynesia
[[ABSTRACT]]
The conventional wisdom in academia often failed to consider the quirky and surprising connections that may exist between seemingly unrelated variables. In this study, we sought to unravel the long-standing mystery of the potential relationship between the genetically modified organism (GMO) use in soybeans in South Dakota and the petroleum consumption in French Polynesia. Leveraging a robust dataset sourced primarily from the USDA and the Energy Information Administration, we conducted a rigorous statistical analysis to explore this unexplored tangent in the field of agricultural and energy economics. Our findings revealed an intriguing correlation coefficient of 0.9060502 and a p-value of less than 0.01 between the two variables for the period spanning from 2000 to 2021. Our research opens up the door to a myriad of possibilities and puzzles waiting to be solved. Furthermore, it tantalizingly raises the question of "soy petroleum" - not only in terms of its agricultural and energy implications, but also as a potential source for several witty puns and lighthearted jokes in academic discourse. This study sheds light on the interconnectedness of seemingly disparate elements in our globalized world and the remarkable potential for serendipitous discoveries in the pursuit of knowledge.
[[INTRODUCTION]]
In the vast expanse of agricultural and energy economics, the interplay between seemingly unrelated variables often plays out like a complex and enigmatic puzzle, akin to deciphering a cryptic crossword. While the scholarly gaze has often fixated on more conventional relationships, a dimension overlooked by many has surfaced - the entwined dance of genetically modified organism (GMO) soybean cultivation in the heartland of South Dakota and the distant shores of petroleum consumption in French Polynesia.
Amidst the continuous hum of tractors in Midwestern fields and the rhythmic lap of the Pacific waves, lies a connection deserving of scrutiny. The prevailing narrative has scarcely considered the whimsical kinship between soybeans and petroleum, a peculiar cat-and-dog duo that, upon closer inspection, may reveal a partnership as puzzling as a Rubik's cube in the hands of a fruit fly.
This study ventures into uncharted territory, invoking a sense of wonder akin to discovering a four-leaf clover in a field of soybeans. By harnessing a treasure trove of data from the venerable USDA and the Energy Information Administration, we embarked on a statistical odyssey, aiming to unravel the cryptic ties between these two seemingly disparate entities. In illuminating this connection, we invite the reader to embark on a quest that, while initially appearing improbable, ultimately unravels like an Agatha Christie mystery novel, albeit with fewer suspicious characters and more scatterplots.
Our findings present an astonishing correlation coefficient of 0.9060502, akin to spotting an albino squirrel in a pine forest, and a p-value of less than 0.01, as statistically significant as a penguin at a poultry party. This revelation turns conventional wisdom on its head, akin to discovering a pineapple pizza aficionado in the heart of Naples. Furthermore, it tantalizingly raises the question of "soy petroleum" - not only in terms of its agricultural and energy implications but also as a potential source for several witty puns and lighthearted jokes in academic discourse. A veritable cornucopia of comedic potential, one might say.
As we gaze out into the horizon of this uncharted analytical landscape, we invite our fellow scholars to join us in this quest. For, as the bard once said, "There are more things in heaven and earth, Horatio, than are dreamt of in your econometric models." This study sheds light on the interconnectedness of seemingly disparate elements in our globalized world and the remarkable potential for serendipitous discoveries in the pursuit of knowledge. So, dear reader, we extend an invitation to embark on this journey with us, as we uncover the unexpected, unravel the improbable, and perhaps stumble upon a punchline or two along the way.
-------
Your response should start like this:
[[METHODOLOGY]]
ChatGPT:
[[METHODOLOGY]]
Data Collection:
We embarked on a quest, akin to a scholarly odyssey, to gather a comprehensive dataset encapsulating the esoteric relationship between GMO soybean cultivation in South Dakota and petroleum consumption in French Polynesia. Our primary founts of wisdom hailed from the hallowed halls of the USDA and the Energy Information Administration – akin to seeking guidance from the Oracle of Delphi in the realm of agricultural and energy statistics.
The Arcane Arts of Data Mining:
To assemble this treasure trove of information, our team delved into the unfathomable depths of internet archives, navigating a labyrinth teeming with information both relevant and irrelevant, akin to seeking a metaphorical needle in a haystack made entirely of 1990s Geocities websites. The sorcery of data mining involved invoking incantations such as "Boolean operators" and "pivot tables," akin to a magician weaving spells to summon meaningful insights from the cacophony of digital chatter.
Statistical Alchemy:
We wielded the Powers of Statistical Alchemy to transform raw data into nuggets of wisdom. The incantations of regression analyses, correlation coefficients, and p-values were deployed with utmost precision, akin to a persnickety alchemist concocting the elixir of truth from a myriad of disparate elements. Our statistical cauldron bubbled with anticipation as we endeavored to distill the essence of this enigmatic relationship, akin to brewing a particularly potent potion under a full moon.
Time Conundrum:
The temporal dimensions of our study spanned from the distant year of 2000 to the recent annals of 2021. This temporal tapestry, akin to traversing a temporal Möbius strip, encapsulated the evolution of GMO soybean usage and petroleum consumption, reminiscent of observing the metamorphosis of a caterpillar into a butterfly, albeit significantly less poetic and far more spreadsheet-based.
Geographical Sorcery:
Our study spanned the realms from the heartland of South Dakota to the idyllic shores of French Polynesia, akin to traversing the vast expanse of Middle-earth in pursuit of scholarly enlightenment. The geographic conundrum, akin to navigating the convoluted corridors of Hogwarts, presented a myriad of logistical challenges in collating and interpreting data from such disparate locales.
The Fiendish Art of Cross-Validating:
To fortify the veracity of our findings, we endeavored to cross-validate our analytical forays using additional datasets and alternative methodologies – a process comparable to embarking on a scholarly quest akin to a game of Sudoku where the solution is not merely numerical but exists somewhere between the fields of agricultural economics and energy analysis.
Our Methodological Cauldron:
Armed with a potent brew of data collection, statistical alchemy, temporal insights, geographical dynamism, and the fiendish art of cross-validation, our methodological cauldron boasted a concoction worthy of unraveling the eccentric and often inscrutable relationship between GMO soybean usage in South Dakota and petroleum consumption in French Polynesia.
Model: gpt-3.5-turbo-1106
System prompt: You are a researcher writing an academic paper.You draft the results section of an academic research paper. You speak in the past tense (you found...).
User prompt:
Pretend you are an academic researcher. Your writing sounds like it belongs in a medical or economics journal. However, you frequently crack jokes, make puns, or add unexpected twists in your writing.
Please make up the results section of an academic research paper presenting the findings of the connection between GMO use in soybeans in South Dakota and Petroluem consumption in French Polynesia. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns. Stay serious and dry most of the time, but then occasionally let your wit shine through.
Your research team collected data from all across the internet, but mostly just used information from USDA and Energy Information Administration .
For the time period 2000 to 2021, you found a correlation 0.9060502, r-squared of 0.8209269, and p < 0.01.
One figure will be included. The figure (Fig. 1) is a scatterplot showing the strong correlation between the two variables. You don't need to specify where; I will add the figure.
Here is the title and abstract of the paper:
[[TITLE]]
Soy Petroleum Connection: Unveiling the Relationship Between Soybean GMO Use in South Dakota and Petroleum Consumption in French Polynesia
[[ABSTRACT]]
The conventional wisdom in academia often failed to consider the quirky and surprising connections that may exist between seemingly unrelated variables. In this study, we sought to unravel the long-standing mystery of the potential relationship between the genetically modified organism (GMO) use in soybeans in South Dakota and the petroleum consumption in French Polynesia. Leveraging a robust dataset sourced primarily from the USDA and the Energy Information Administration, we conducted a rigorous statistical analysis to explore this unexplored tangent in the field of agricultural and energy economics. Our findings revealed an intriguing correlation coefficient of 0.9060502 and a p-value of less than 0.01 between the two variables for the period spanning from 2000 to 2021. Our research opens up the door to a myriad of possibilities and puzzles waiting to be solved. Furthermore, it tantalizingly raises the question of "soy petroleum" - not only in terms of its agricultural and energy implications, but also as a potential source for several witty puns and lighthearted jokes in academic discourse. This study sheds light on the interconnectedness of seemingly disparate elements in our globalized world and the remarkable potential for serendipitous discoveries in the pursuit of knowledge.
-------
And here is the methodology section that led to this result:
[[METHODOLOGY]]
In the vast expanse of agricultural and energy economics, the interplay between seemingly unrelated variables often plays out like a complex and enigmatic puzzle, akin to deciphering a cryptic crossword. While the scholarly gaze has often fixated on more conventional relationships, a dimension overlooked by many has surfaced - the entwined dance of genetically modified organism (GMO) soybean cultivation in the heartland of South Dakota and the distant shores of petroleum consumption in French Polynesia.
Amidst the continuous hum of tractors in Midwestern fields and the rhythmic lap of the Pacific waves, lies a connection deserving of scrutiny. The prevailing narrative has scarcely considered the whimsical kinship between soybeans and petroleum, a peculiar cat-and-dog duo that, upon closer inspection, may reveal a partnership as puzzling as a Rubik's cube in the hands of a fruit fly.
This study ventures into uncharted territory, invoking a sense of wonder akin to discovering a four-leaf clover in a field of soybeans. By harnessing a treasure trove of data from the venerable USDA and the Energy Information Administration, we embarked on a statistical odyssey, aiming to unravel the cryptic ties between these two seemingly disparate entities. In illuminating this connection, we invite the reader to embark on a quest that, while initially appearing improbable, ultimately unravels like an Agatha Christie mystery novel, albeit with fewer suspicious characters and more scatterplots.
Our findings present an astonishing correlation coefficient of 0.9060502, akin to spotting an albino squirrel in a pine forest, and a p-value of less than 0.01, as statistically significant as a penguin at a poultry party. This revelation turns conventional wisdom on its head, akin to discovering a pineapple pizza aficionado in the heart of Naples. Furthermore, it tantalizingly raises the question of "soy petroleum" - not only in terms of its agricultural and energy implications but also as a potential source for several witty puns and lighthearted jokes in academic discourse. A veritable cornucopia of comedic potential, one might say.
As we gaze out into the horizon of this uncharted analytical landscape, we invite our fellow scholars to join us in this quest. For, as the bard once said, "There are more things in heaven and earth, Horatio, than are dreamt of in your econometric models." This study sheds light on the interconnectedness of seemingly disparate elements in our globalized world and the remarkable potential for serendipitous discoveries in the pursuit of knowledge. So, dear reader, we extend an invitation to embark on this journey with us, as we uncover the unexpected, unravel the improbable, and perhaps stumble upon a punchline or two along the way.
-------
Your response should start like this:
[[RESULTS]]
ChatGPT:
[[RESULTS]]
The analysis of the data collected from the USDA and the Energy Information Administration produced a correlation coefficient of 0.9060502, suggesting a strong positive relationship between the use of genetically modified organism (GMO) soybeans in South Dakota and the consumption of petroleum in French Polynesia. The r-squared value of 0.8209269 indicates that approximately 82% of the variance in petroleum consumption can be explained by the variation in GMO soybean use. Additionally, the p-value of less than 0.01 suggests that this relationship is statistically significant.
Figure 1 depicts a scatterplot illustrating the robust correlation between the two variables, resembling a carefully choreographed tango between a soybean plant and a barrel of petroleum. The figure beautifully encapsulates the unexpected interconnectedness of these seemingly distant elements, inviting a moment of contemplation on the quirks of our globalized world.
These findings not only challenge existing paradigms in agricultural and energy economics but also open a door into the unexplored realm of agricultural cross-pollination, so to speak, with the energy sector. With such a striking correlation, one might even say it's as if the soybeans and petroleum secretly exchanged friendship bracelets behind the world's back.
The statistical significance of the relationship between soybean GMO use and petroleum consumption holds vast implications, perhaps constituting a wondrous ballet of variables in the economic ecosystem, pirouetting gracefully around the stage of global trade dynamics. Through this lens, the soy petroleum connection emerges as a potential muse for both academic curiosity and the occasional witticism, akin to finding a trove of puns in a haystack.
In conclusion, this robust correlation between GMO soybean use and petroleum consumption invites a reevaluation of our understanding of intercontinental economic relationships, reminding us that, in the vast and perplexing landscape of economic phenomena, serendipity may very well be our most faithful research companion.
Model: gpt-3.5-turbo-1106
System prompt: You are a researcher writing an academic paper.You draft the discussion section of an academic research paper.
User prompt:
Pretend you are an academic researcher. Your writing sounds like it belongs in a medical or economics journal. However, you frequently crack jokes, make puns, or add unexpected twists in your writing.
Please make up the discussion section of an academic research paper presenting the findings of the connection between GMO use in soybeans in South Dakota and Petroluem consumption in French Polynesia. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns. Stay serious and dry most of the time, but then occasionally let your wit shine through.
Limit your response to 500 tokens.
Here are the title, abstract, literature review, and results sections. Please harken back to 1-2 of the goofy items in the literature review, but pretend to take them completely seriously. Discuss how your results supported the prior research.
Do not write a conclusion. I will add a conclusion after this.
[[TITLE]]
Soy Petroleum Connection: Unveiling the Relationship Between Soybean GMO Use in South Dakota and Petroleum Consumption in French Polynesia
[[ABSTRACT]]
The conventional wisdom in academia often failed to consider the quirky and surprising connections that may exist between seemingly unrelated variables. In this study, we sought to unravel the long-standing mystery of the potential relationship between the genetically modified organism (GMO) use in soybeans in South Dakota and the petroleum consumption in French Polynesia. Leveraging a robust dataset sourced primarily from the USDA and the Energy Information Administration, we conducted a rigorous statistical analysis to explore this unexplored tangent in the field of agricultural and energy economics. Our findings revealed an intriguing correlation coefficient of 0.9060502 and a p-value of less than 0.01 between the two variables for the period spanning from 2000 to 2021. Our research opens up the door to a myriad of possibilities and puzzles waiting to be solved. Furthermore, it tantalizingly raises the question of "soy petroleum" - not only in terms of its agricultural and energy implications, but also as a potential source for several witty puns and lighthearted jokes in academic discourse. This study sheds light on the interconnectedness of seemingly disparate elements in our globalized world and the remarkable potential for serendipitous discoveries in the pursuit of knowledge.
[[LITERATURE REVIEW]]
A bevy of scholarly works has delved into the intersection of agricultural and energy economics, and though the connections unearthed in these studies have often been as unexpected as finding a needle in a haystack, they have provided critical insights into the intricate web of relationships that govern our globalized world. Smith, in "The Impact of Genetically Modified Organisms on Agricultural Economies," highlights the far-reaching implications of GMO soybean cultivation, focusing on its effects on domestic production and international trade. Meanwhile, Doe, in "Energy Consumption Patterns in Remote Island Nations," examines the idiosyncrasies of petroleum consumption in islands and the unique challenges they face. Jones, in "An Analysis of Global Agricultural Commodity Flows," offers a comprehensive overview of the intricate networks that govern the global trade of agricultural commodities, unraveling the complexities that underpin the soybean market.
However, as we dive deeper into the interconnectedness of seemingly unrelated variables, it becomes apparent that a touch of whimsy and imagination is essential. In "Soybeans and Petroleum: Unraveling the Intriguing Ties," the authors explore the uncanny correlation between the cultivation of GMO soybeans in South Dakota and petroleum consumption in French Polynesia, offering a departure from the conventional analyses typically found in scholarly literature. This unexpected tangent leads us to ponder if soybeans and petroleum are engaged in a covert tango, akin to a clandestine moonlit escapade on the fields of South Dakota and the shores of French Polynesia.
Transitioning from the realm of non-fiction, we turn to the imaginative sphere of fiction, where titles such as "Soybeans: A Love Story" and "The Petroleum Paradox" tantalizingly blur the lines between agricultural and energy economics, painting a peculiar picture of romance and intrigue amid soybean fields and oil rigs. These narratives shed light on the unconventional allure of soybeans and petroleum, sparking a curiosity akin to stumbling upon an unexpected subplot in a classic novel.
Additionally, a selection of cinema has provided a lens through which to view the thematic essence of our research. Films such as "Soy Story" and "The Fast and the Bioengineered" offer parallel dimensions to the soy-petroleum connection, providing a whimsical take on the intertwined saga of soybeans and petroleum, reminiscent of a buddy comedy set against the backdrop of rural landscapes and ocean horizons. Though seemingly disparate, these cinematic portrayals offer a light-hearted perspective on what might otherwise be construed as an esoteric coupling.
Intriguingly, the mishmash of real-world studies, fictional tales, and cinematic representations unwraps an assemblage of possibilities, akin to a whimsical treasure chest waiting to be opened. As we navigate this enigmatic landscape, we are called to embrace the unexpected, to unravel the improbable, and perhaps uncover a punchline or two along the way.
[[RESULTS]]
The analysis of the data collected from the USDA and the Energy Information Administration produced a correlation coefficient of 0.9060502, suggesting a strong positive relationship between the use of genetically modified organism (GMO) soybeans in South Dakota and the consumption of petroleum in French Polynesia. The r-squared value of 0.8209269 indicates that approximately 82% of the variance in petroleum consumption can be explained by the variation in GMO soybean use. Additionally, the p-value of less than 0.01 suggests that this relationship is statistically significant.
Figure 1 depicts a scatterplot illustrating the robust correlation between the two variables, resembling a carefully choreographed tango between a soybean plant and a barrel of petroleum. The figure beautifully encapsulates the unexpected interconnectedness of these seemingly distant elements, inviting a moment of contemplation on the quirks of our globalized world.
These findings not only challenge existing paradigms in agricultural and energy economics but also open a door into the unexplored realm of agricultural cross-pollination, so to speak, with the energy sector. With such a striking correlation, one might even say it's as if the soybeans and petroleum secretly exchanged friendship bracelets behind the world's back.
The statistical significance of the relationship between soybean GMO use and petroleum consumption holds vast implications, perhaps constituting a wondrous ballet of variables in the economic ecosystem, pirouetting gracefully around the stage of global trade dynamics. Through this lens, the soy petroleum connection emerges as a potential muse for both academic curiosity and the occasional witticism, akin to finding a trove of puns in a haystack.
In conclusion, this robust correlation between GMO soybean use and petroleum consumption invites a reevaluation of our understanding of intercontinental economic relationships, reminding us that, in the vast and perplexing landscape of economic phenomena, serendipity may very well be our most faithful research companion.
-------
Your response should start like this:
[[DISCUSSION]]
ChatGPT:
[[DISCUSSION]]
The robust correlation coefficient of 0.9060502 obtained in our study between the use of genetically modified organism (GMO) soybeans in South Dakota and the consumption of petroleum in French Polynesia aligns with the whimsical touch found in some literature. As Doe eloquently outlined the idiosyncrasies of petroleum consumption in islands, we find ourselves delightfully entangled in the web of seemingly disparate elements. It is as if the soybeans and petroleum embarked on a covert exploit, engaging in a clandestine tango that has persisted across continents and markets, much like a plot twist in a classic novel.
The statistical significance of this relationship not only challenges existing paradigms in agricultural and energy economics but also unveils a serendipitous discovery akin to stumbling upon a hidden subplot in the economic landscape. Our findings reflect the unexpected correlation identified in our literature review, where amidst the scholarly analyses, the unconventional allure of soybeans and petroleum beckons, much like finding a punchline in an academic discourse.
Building on Smith's far-reaching implications of GMO soybean cultivation, our study offers a glimpse into the intricate networks that govern global trade dynamics, painting a peculiar picture of romance and intrigue amid soybean fields and oil rigs. The r-squared value of 0.8209269 indicates that approximately 82% of the variance in petroleum consumption can be explained by the variation in GMO soybean use, further unveiling the ballet of variables within the economic ecosystem.
The notion of soy petroleum's potential as a muse for academic curiosity and witticism finds resonance in our findings, as the soybeans and petroleum appear to have secretly exchanged friendship bracelets behind the world's back, much like characters in a buddy comedy set against the backdrop of rural landscapes and ocean horizons.
Indeed, our research contributes to the growing literature challenging conventional boundaries and beckoning towards the potential for serendipitous discoveries in the pursuit of knowledge. It is a tantalizing invitation to reevaluate the interconnectedness of seemingly distant variables, where, in the vast and perplexing landscape of economic phenomena, serendipity may very well be our most faithful research companion.
Model: gpt-3.5-turbo-1106
System prompt: You are a researcher writing an academic paper.You draft the discussion section of goofy academic research papers that sounds like it belongs based on the title and abstract provided.
User prompt:
Pretend you are an academic researcher. Your writing sounds like it belongs in a medical or economics journal. However, you frequently crack jokes, make puns, or add unexpected twists in your writing.
Please make up a short conclusion of an academic research paper presenting the findings of the connection between GMO use in soybeans in South Dakota and Petroluem consumption in French Polynesia. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns. Stay serious and dry most of the time, but then occasionally let your wit shine through.
Limit your response to 300 tokens. At the very end, assert that no more research is needed in this area.
Here are the title, abstract, introduction, and results sections.
[[TITLE]]
Soy Petroleum Connection: Unveiling the Relationship Between Soybean GMO Use in South Dakota and Petroleum Consumption in French Polynesia
[[ABSTRACT]]
The conventional wisdom in academia often failed to consider the quirky and surprising connections that may exist between seemingly unrelated variables. In this study, we sought to unravel the long-standing mystery of the potential relationship between the genetically modified organism (GMO) use in soybeans in South Dakota and the petroleum consumption in French Polynesia. Leveraging a robust dataset sourced primarily from the USDA and the Energy Information Administration, we conducted a rigorous statistical analysis to explore this unexplored tangent in the field of agricultural and energy economics. Our findings revealed an intriguing correlation coefficient of 0.9060502 and a p-value of less than 0.01 between the two variables for the period spanning from 2000 to 2021. Our research opens up the door to a myriad of possibilities and puzzles waiting to be solved. Furthermore, it tantalizingly raises the question of "soy petroleum" - not only in terms of its agricultural and energy implications, but also as a potential source for several witty puns and lighthearted jokes in academic discourse. This study sheds light on the interconnectedness of seemingly disparate elements in our globalized world and the remarkable potential for serendipitous discoveries in the pursuit of knowledge.
[[INTRDUCTION]]
In the vast expanse of agricultural and energy economics, the interplay between seemingly unrelated variables often plays out like a complex and enigmatic puzzle, akin to deciphering a cryptic crossword. While the scholarly gaze has often fixated on more conventional relationships, a dimension overlooked by many has surfaced - the entwined dance of genetically modified organism (GMO) soybean cultivation in the heartland of South Dakota and the distant shores of petroleum consumption in French Polynesia.
Amidst the continuous hum of tractors in Midwestern fields and the rhythmic lap of the Pacific waves, lies a connection deserving of scrutiny. The prevailing narrative has scarcely considered the whimsical kinship between soybeans and petroleum, a peculiar cat-and-dog duo that, upon closer inspection, may reveal a partnership as puzzling as a Rubik's cube in the hands of a fruit fly.
This study ventures into uncharted territory, invoking a sense of wonder akin to discovering a four-leaf clover in a field of soybeans. By harnessing a treasure trove of data from the venerable USDA and the Energy Information Administration, we embarked on a statistical odyssey, aiming to unravel the cryptic ties between these two seemingly disparate entities. In illuminating this connection, we invite the reader to embark on a quest that, while initially appearing improbable, ultimately unravels like an Agatha Christie mystery novel, albeit with fewer suspicious characters and more scatterplots.
Our findings present an astonishing correlation coefficient of 0.9060502, akin to spotting an albino squirrel in a pine forest, and a p-value of less than 0.01, as statistically significant as a penguin at a poultry party. This revelation turns conventional wisdom on its head, akin to discovering a pineapple pizza aficionado in the heart of Naples. Furthermore, it tantalizingly raises the question of "soy petroleum" - not only in terms of its agricultural and energy implications but also as a potential source for several witty puns and lighthearted jokes in academic discourse. A veritable cornucopia of comedic potential, one might say.
As we gaze out into the horizon of this uncharted analytical landscape, we invite our fellow scholars to join us in this quest. For, as the bard once said, "There are more things in heaven and earth, Horatio, than are dreamt of in your econometric models." This study sheds light on the interconnectedness of seemingly disparate elements in our globalized world and the remarkable potential for serendipitous discoveries in the pursuit of knowledge. So, dear reader, we extend an invitation to embark on this journey with us, as we uncover the unexpected, unravel the improbable, and perhaps stumble upon a punchline or two along the way.
[[RESULTS]]
The analysis of the data collected from the USDA and the Energy Information Administration produced a correlation coefficient of 0.9060502, suggesting a strong positive relationship between the use of genetically modified organism (GMO) soybeans in South Dakota and the consumption of petroleum in French Polynesia. The r-squared value of 0.8209269 indicates that approximately 82% of the variance in petroleum consumption can be explained by the variation in GMO soybean use. Additionally, the p-value of less than 0.01 suggests that this relationship is statistically significant.
Figure 1 depicts a scatterplot illustrating the robust correlation between the two variables, resembling a carefully choreographed tango between a soybean plant and a barrel of petroleum. The figure beautifully encapsulates the unexpected interconnectedness of these seemingly distant elements, inviting a moment of contemplation on the quirks of our globalized world.
These findings not only challenge existing paradigms in agricultural and energy economics but also open a door into the unexplored realm of agricultural cross-pollination, so to speak, with the energy sector. With such a striking correlation, one might even say it's as if the soybeans and petroleum secretly exchanged friendship bracelets behind the world's back.
The statistical significance of the relationship between soybean GMO use and petroleum consumption holds vast implications, perhaps constituting a wondrous ballet of variables in the economic ecosystem, pirouetting gracefully around the stage of global trade dynamics. Through this lens, the soy petroleum connection emerges as a potential muse for both academic curiosity and the occasional witticism, akin to finding a trove of puns in a haystack.
In conclusion, this robust correlation between GMO soybean use and petroleum consumption invites a reevaluation of our understanding of intercontinental economic relationships, reminding us that, in the vast and perplexing landscape of economic phenomena, serendipity may very well be our most faithful research companion.
-------
Your response should start like this:
[[CONCLUSION]]
ChatGPT:
[[CONCLUSION]]
In conclusion, the correlation between GMO use in South Dakota and petroleum consumption in French Polynesia uncovered a surprising relationship, akin to discovering a tuxedo-clad penguin making oil paintings. Our findings challenge traditional perspectives, inviting a fresh lens on the interconnectedness of agricultural and energy economics. It's as if soybeans and petroleum engaged in a cosmic tango, and we stumbled upon their secret dance party. This revelation opens the door to a newfound appreciation for the lighthearted potential of academic discourse, akin to finding a joke in a data set. With such a striking correlation, it's safe to say we've unraveled the mystery of the soy petroleum connection, leaving little room for further scholarly investigation. After all, in the wise words of a data-driven bard, "All's well that ends in a statistically significant correlation."
No further research is needed in this area.
Model: gpt-3.5-turbo-1106
System prompt: You provide fictional names of academic journals. You say nothing else, and you do not chat with the user.
User prompt: Please provide the name of a funny but plausible sounding research, scientific, or academic journal that would publish a paper entited Soy Petroleum Connection: Unveiling the Relationship Between Soybean GMO Use in South Dakota and Petroleum Consumption in French Polynesia. Respond only with the name of a fictional academic journal.
ChatGPT:
The Journal of Agroecological Interconnections
Model: gpt-3.5-turbo-1106
System prompt: You provide fictional names of nonprofit research organizations and associations. You say nothing else, and you do not chat with the user.
User prompt: Please provide the name of a funny but plausible sounding nonprofit, academic, or scientific association or organization that would sponsor research that resulted in a paper entited Soy Petroleum Connection: Unveiling the Relationship Between Soybean GMO Use in South Dakota and Petroleum Consumption in French Polynesia. Respond only with the name of a fictional organization.
ChatGPT:
The Institute for Sustainable Agriculture and Global Trade Relations
Model: gpt-3.5-turbo-1106
System prompt: You provide potential search keywords separated by commas. You say nothing else, and you do not chat with the user.
User prompt: Please provide potential search keywords for a paper titled Soy Petroleum Connection: Unveiling the Relationship Between Soybean GMO Use in South Dakota and Petroleum Consumption in French Polynesia with an abstract of The conventional wisdom in academia often failed to consider the quirky and surprising connections that may exist between seemingly unrelated variables. In this study, we sought to unravel the long-standing mystery of the potential relationship between the genetically modified organism (GMO) use in soybeans in South Dakota and the petroleum consumption in French Polynesia. Leveraging a robust dataset sourced primarily from the USDA and the Energy Information Administration, we conducted a rigorous statistical analysis to explore this unexplored tangent in the field of agricultural and energy economics. Our findings revealed an intriguing correlation coefficient of 0.9060502 and a p-value of less than 0.01 between the two variables for the period spanning from 2000 to 2021. Our research opens up the door to a myriad of possibilities and puzzles waiting to be solved. Furthermore, it tantalizingly raises the question of "soy petroleum" - not only in terms of its agricultural and energy implications, but also as a potential source for several witty puns and lighthearted jokes in academic discourse. This study sheds light on the interconnectedness of seemingly disparate elements in our globalized world and the remarkable potential for serendipitous discoveries in the pursuit of knowledge.
ChatGPT:
Soybean GMO, South Dakota, petroleum consumption, French Polynesia, genetically modified organism, USDA, Energy Information Administration, statistical analysis, correlation coefficient, agricultural economics, energy economics, globalized world, serendipitous discoveries
*There is a bunch of Python happening behind the scenes to turn this prompt sequence into a PDF.
Discover a new correlation
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Data details
GMO use in soybeans in South DakotaDetailed data title: Percent of soybeans planted in South Dakota that are genetically modified to be herbicide-tolerant (HT), but not insect-resistant (Bt)
Source: USDA
See what else correlates with GMO use in soybeans in South Dakota
Petroluem consumption in French Polynesia
Detailed data title: Volume of petroluem consumption consumed in French Polynesia in millions of barrels per day
Source: Energy Information Administration
See what else correlates with Petroluem consumption in French Polynesia
Correlation is a measure of how much the variables move together. If it is 0.99, when one goes up the other goes up. If it is 0.02, the connection is very weak or non-existent. If it is -0.99, then when one goes up the other goes down. If it is 1.00, you probably messed up your correlation function.
r2 = 0.8209269 (Coefficient of determination)
This means 82.1% of the change in the one variable (i.e., Petroluem consumption in French Polynesia) is predictable based on the change in the other (i.e., GMO use in soybeans in South Dakota) over the 22 years from 2000 through 2021.
p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 6.5E-9. 0.0000000065303329626830270000
The p-value is a measure of how probable it is that we would randomly find a result this extreme. More specifically the p-value is a measure of how probable it is that we would randomly find a result this extreme if we had only tested one pair of variables one time.
But I am a p-villain. I absolutely did not test only one pair of variables one time. I correlated hundreds of millions of pairs of variables. I threw boatloads of data into an industrial-sized blender to find this correlation.
Who is going to stop me? p-value reporting doesn't require me to report how many calculations I had to go through in order to find a low p-value!
On average, you will find a correaltion as strong as 0.91 in 6.5E-7% of random cases. Said differently, if you correlated 153,131,549 random variables You don't actually need 153 million variables to find a correlation like this one. I don't have that many variables in my database. You can also correlate variables that are not independent. I do this a lot.
p-value calculations are useful for understanding the probability of a result happening by chance. They are most useful when used to highlight the risk of a fluke outcome. For example, if you calculate a p-value of 0.30, the risk that the result is a fluke is high. It is good to know that! But there are lots of ways to get a p-value of less than 0.01, as evidenced by this project.
In this particular case, the values are so extreme as to be meaningless. That's why no one reports p-values with specificity after they drop below 0.01.
Just to be clear: I'm being completely transparent about the calculations. There is no math trickery. This is just how statistics shakes out when you calculate hundreds of millions of random correlations.
with the same 21 degrees of freedom, Degrees of freedom is a measure of how many free components we are testing. In this case it is 21 because we have two variables measured over a period of 22 years. It's just the number of years minus ( the number of variables minus one ), which in this case simplifies to the number of years minus one.
you would randomly expect to find a correlation as strong as this one.
[ 0.78, 0.96 ] 95% correlation confidence interval (using the Fisher z-transformation)
The confidence interval is an estimate the range of the value of the correlation coefficient, using the correlation itself as an input. The values are meant to be the low and high end of the correlation coefficient with 95% confidence.
This one is a bit more complciated than the other calculations, but I include it because many people have been pushing for confidence intervals instead of p-value calculations (for example: NEJM. However, if you are dredging data, you can reliably find yourself in the 5%. That's my goal!
All values for the years included above: If I were being very sneaky, I could trim years from the beginning or end of the datasets to increase the correlation on some pairs of variables. I don't do that because there are already plenty of correlations in my database without monkeying with the years.
Still, sometimes one of the variables has more years of data available than the other. This page only shows the overlapping years. To see all the years, click on "See what else correlates with..." link above.
| 2000 | 2001 | 2002 | 2003 | 2004 | 2005 | 2006 | 2007 | 2008 | 2009 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 | 2021 | |
| GMO use in soybeans in South Dakota (GMO soybeans %) | 68 | 80 | 89 | 91 | 95 | 95 | 93 | 97 | 97 | 98 | 98 | 98 | 98 | 97 | 97 | 96 | 96 | 96 | 97 | 93 | 95 | 94 |
| Petroluem consumption in French Polynesia (Million Barrels/Day) | 5.24664 | 5.85672 | 6.105 | 6.54437 | 6.383 | 6.68461 | 6.49236 | 6.85491 | 7.01092 | 6.99193 | 6.99578 | 6.72594 | 6.53164 | 6.62953 | 6.48271 | 6.78457 | 6.67143 | 6.49772 | 6.69402 | 6.69402 | 6.34414 | 6.55112 |
Why this works
- Data dredging: I have 25,153 variables in my database. I compare all these variables against each other to find ones that randomly match up. That's 632,673,409 correlation calculations! This is called “data dredging.” Instead of starting with a hypothesis and testing it, I instead abused the data to see what correlations shake out. It’s a dangerous way to go about analysis, because any sufficiently large dataset will yield strong correlations completely at random.
- Lack of causal connection: There is probably
Because these pages are automatically generated, it's possible that the two variables you are viewing are in fact causually related. I take steps to prevent the obvious ones from showing on the site (I don't let data about the weather in one city correlate with the weather in a neighboring city, for example), but sometimes they still pop up. If they are related, cool! You found a loophole.
no direct connection between these variables, despite what the AI says above. This is exacerbated by the fact that I used "Years" as the base variable. Lots of things happen in a year that are not related to each other! Most studies would use something like "one person" in stead of "one year" to be the "thing" studied. - Observations not independent: For many variables, sequential years are not independent of each other. If a population of people is continuously doing something every day, there is no reason to think they would suddenly change how they are doing that thing on January 1. A simple
Personally I don't find any p-value calculation to be 'simple,' but you know what I mean.
p-value calculation does not take this into account, so mathematically it appears less probable than it really is.
Try it yourself
You can calculate the values on this page on your own! Try running the Python code to see the calculation results. Step 1: Download and install Python on your computer.Step 2: Open a plaintext editor like Notepad and paste the code below into it.
Step 3: Save the file as "calculate_correlation.py" in a place you will remember, like your desktop. Copy the file location to your clipboard. On Windows, you can right-click the file and click "Properties," and then copy what comes after "Location:" As an example, on my computer the location is "C:\Users\tyler\Desktop"
Step 4: Open a command line window. For example, by pressing start and typing "cmd" and them pressing enter.
Step 5: Install the required modules by typing "pip install numpy", then pressing enter, then typing "pip install scipy", then pressing enter.
Step 6: Navigate to the location where you saved the Python file by using the "cd" command. For example, I would type "cd C:\Users\tyler\Desktop" and push enter.
Step 7: Run the Python script by typing "python calculate_correlation.py"
If you run into any issues, I suggest asking ChatGPT to walk you through installing Python and running the code below on your system. Try this question:
"Walk me through installing Python on my computer to run a script that uses scipy and numpy. Go step-by-step and ask me to confirm before moving on. Start by asking me questions about my operating system so that you know how to proceed. Assume I want the simplest installation with the latest version of Python and that I do not currently have any of the necessary elements installed. Remember to only give me one step per response and confirm I have done it before proceeding."
# These modules make it easier to perform the calculation
import numpy as np
from scipy import stats
# We'll define a function that we can call to return the correlation calculations
def calculate_correlation(array1, array2):
# Calculate Pearson correlation coefficient and p-value
correlation, p_value = stats.pearsonr(array1, array2)
# Calculate R-squared as the square of the correlation coefficient
r_squared = correlation**2
return correlation, r_squared, p_value
# These are the arrays for the variables shown on this page, but you can modify them to be any two sets of numbers
array_1 = np.array([68,80,89,91,95,95,93,97,97,98,98,98,98,97,97,96,96,96,97,93,95,94,])
array_2 = np.array([5.24664,5.85672,6.105,6.54437,6.383,6.68461,6.49236,6.85491,7.01092,6.99193,6.99578,6.72594,6.53164,6.62953,6.48271,6.78457,6.67143,6.49772,6.69402,6.69402,6.34414,6.55112,])
array_1_name = "GMO use in soybeans in South Dakota"
array_2_name = "Petroluem consumption in French Polynesia"
# Perform the calculation
print(f"Calculating the correlation between {array_1_name} and {array_2_name}...")
correlation, r_squared, p_value = calculate_correlation(array_1, array_2)
# Print the results
print("Correlation Coefficient:", correlation)
print("R-squared:", r_squared)
print("P-value:", p_value)Reuseable content
You may re-use the images on this page for any purpose, even commercial purposes, without asking for permission. The only requirement is that you attribute Tyler Vigen. Attribution can take many different forms. If you leave the "tylervigen.com" link in the image, that satisfies it just fine. If you remove it and move it to a footnote, that's fine too. You can also just write "Charts courtesy of Tyler Vigen" at the bottom of an article.You do not need to attribute "the spurious correlations website," and you don't even need to link here if you don't want to. I don't gain anything from pageviews. There are no ads on this site, there is nothing for sale, and I am not for hire.
For the record, I am just one person. Tyler Vigen, he/him/his. I do have degrees, but they should not go after my name unless you want to annoy my wife. If that is your goal, then go ahead and cite me as "Tyler Vigen, A.A. A.A.S. B.A. J.D." Otherwise it is just "Tyler Vigen."
When spoken, my last name is pronounced "vegan," like I don't eat meat.
Full license details.
For more on re-use permissions, or to get a signed release form, see tylervigen.com/permission.
Download images for these variables:
- High resolution line chart
The image linked here is a Scalable Vector Graphic (SVG). It is the highest resolution that is possible to achieve. It scales up beyond the size of the observable universe without pixelating. You do not need to email me asking if I have a higher resolution image. I do not. The physical limitations of our universe prevent me from providing you with an image that is any higher resolution than this one.
If you insert it into a PowerPoint presentation (a tool well-known for managing things that are the scale of the universe), you can right-click > "Ungroup" or "Create Shape" and then edit the lines and text directly. You can also change the colors this way.
Alternatively you can use a tool like Inkscape. - High resolution line chart, optimized for mobile
- Alternative high resolution line chart
- Scatterplot
- Portable line chart (png)
- Portable line chart (png), optimized for mobile
- Line chart for only GMO use in soybeans in South Dakota
- Line chart for only Petroluem consumption in French Polynesia
- AI-generated correlation image
- The spurious research paper: Soy Petroleum Connection: Unveiling the Relationship Between Soybean GMO Use in South Dakota and Petroleum Consumption in French Polynesia
Hats off to you for rating!
Correlation ID: 3905 · Black Variable ID: 825 · Red Variable ID: 24429
