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AI explanation
As GMO corn in Kansas thrived, it sent out strong 'kernel' signals to plants all around the world, including in Qatar. This led to a corny chain reaction, ultimately powering up the biomass industry in Qatar. It's a-maize-ing how a little genetic modification can husk a whole lot of energy!
Model: dalle-3
Prompt: Generate an image of a vast, golden Kansas landscape with towering, genetically modified cornstalks swaying in the wind. On the horizon, depict a sprawling power plant in Qatar emitting clean and sustainable biomass energy. Use the style of charcoal to capture the striking contrast between the natural beauty and industrial innovation in the scene. 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 corn grown in Kansas and Biomass power generated in Qatar. 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: Vaporwave Charcoal Glitch art Cute cartoon Data cyber art
ChatGPT: Charcoal
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 corn grown in Kansas and Biomass power generated in Qatar together. Keep the description simple.
ChatGPT: In the golden fields of Kansas, tall stalks of genetically modified corn sway in the wind, their vibrant green leaves shimmering in the sunlight. Next to the corn fields, in Qatar, a large power plant hums with activity, burning a mix of agricultural waste and other organic materials to generate clean and sustainable biomass power.
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: [ Charcoal ] and depicting this scene: In the golden fields of Kansas, tall stalks of genetically modified corn sway in the wind, their vibrant green leaves shimmering in the sunlight. Next to the corn fields, in Qatar, a large power plant hums with activity, burning a mix of agricultural waste and other organic materials to generate clean and sustainable biomass power. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Generate an image of a vast, golden Kansas landscape with towering, genetically modified cornstalks swaying in the wind. On the horizon, depict a sprawling power plant in Qatar emitting clean and sustainable biomass energy. Use the style of charcoal to capture the striking contrast between the natural beauty and industrial innovation in the scene.
*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 corn grown in Kansas caused Biomass power generated in Qatar to increase. Include one good pun.
AI academic paper
 |
Husk and Muscle: The Corny Connection between GMOs in Kansas and Biomass Power in Qatar The Journal of Global Agricultural Innovation Jan 2024 Reminder: This paper is AI-generated. Not real! |
Model: gpt-3.5-turbo-1106
System prompt: You are a dry but witty academic researcher. You speak in the passive voice, and your writing sounds like it belongs in a medical or economics journal.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 corn grown in Kansas and Biomass power generated in Qatar. You can't resist a good dad joke. Add a relevant dad joke related to the content every couple of sentences.
Make the title a clever pun.
Your research team used data from USDA and Energy Information Administration to assess this nagging question. You found a correlation coefficient of 0.9756072 and p < 0.01 for 2012 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]]
Husk and Muscle: The Corny Connection between GMOs in Kansas and Biomass Power in Qatar
[[ABSTRACT]]
This study delves into the intriguing relationship between the adoption of genetically modified organisms (GMOs) in corn cultivation in Kansas and the generation of biomass power in Qatar. While this topic may seem as unrelated as corn and electricity, our research reveals a striking correlation that cannot be brushed aside. Utilizing data from the USDA and the Energy Information Administration, our team employed rigorous statistical analysis to scrutinize this unlikely bond.
With a correlation coefficient of 0.9756072 and a p-value < 0.01 for the period spanning 2012 to 2021, the findings astound and may leave some scratching their heads – much like a stalk of corn. A kernel of truth is unearthed in the murky soil of causality, shedding light on the unforeseen interplay between agricultural practices and energy outcomes. The results of this study not only illustrate the interconnectedness of global systems but also highlight the cornucopia of unexpected relationships waiting to be unearthed in the world of research.
Speaking of corn, did you hear about the farmer who was outstanding in his field? His corn was all ears.
Model: gpt-3.5-turbo-1106
System prompt: You are a dry but witty academic researcher. You speak in the passive voice, and your writing sounds like it belongs in a medical or economics journal.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 corn grown in Kansas and Biomass power generated in Qatar. You can't resist a good dad joke. Add a relevant dad joke related to the content every couple of sentences. Sneak in some jokes and clever puns about science, research, statistics, and the variables.
Here is the title and abstract of the paper:
[[TITLE]]
Husk and Muscle: The Corny Connection between GMOs in Kansas and Biomass Power in Qatar
[[ABSTRACT]]
This study delves into the intriguing relationship between the adoption of genetically modified organisms (GMOs) in corn cultivation in Kansas and the generation of biomass power in Qatar. While this topic may seem as unrelated as corn and electricity, our research reveals a striking correlation that cannot be brushed aside. Utilizing data from the USDA and the Energy Information Administration, our team employed rigorous statistical analysis to scrutinize this unlikely bond.
With a correlation coefficient of 0.9756072 and a p-value < 0.01 for the period spanning 2012 to 2021, the findings astound and may leave some scratching their heads – much like a stalk of corn. A kernel of truth is unearthed in the murky soil of causality, shedding light on the unforeseen interplay between agricultural practices and energy outcomes. The results of this study not only illustrate the interconnectedness of global systems but also highlight the cornucopia of unexpected relationships waiting to be unearthed in the world of research.
Speaking of corn, did you hear about the farmer who was outstanding in his field? His corn was all ears.
-------
Your response should start like this:
[[INTRODUCTION]]
ChatGPT:
[[INTRODUCTION]]
The relationship between genetically modified organisms (GMOs) in corn production and biomass power generation may seem as out of place as a corn cob at a power plant, but our study reveals a connection that is more than just a-maize-ing. While this subject may sound more like a kernel of corny humor than a serious research topic, the statistical evidence speaks for itself - much like the wisdom of a wise old ear of corn.
As researchers, it is our duty to peel back the layers of complexity, much like peeling back the husk of corn, in order to shed light on the unexpected ties that bind seemingly disparate phenomena. It is not merely an academic pursuit; it's a-maize-ing what can be gleaned from delving into the fields of science and research.
The statistical relationship uncovered between the adoption of GMOs in Kansas and the production of biomass power in Qatar is as surprising as finding a kernel of corn in the desert sands. It's clear that this study has the potential to pop some conventional wisdom like popcorn, and reveal insights that go against the grain of commonly held assumptions.
Did you hear about the statistician who drowned in a river with an average depth of 3 feet? He was using a mean to an end!
Model: gpt-3.5-turbo-1106
System prompt: You are a dry but witty academic researcher. You speak in the passive voice, and your writing sounds like it belongs in a medical or economics journal.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 corn grown in Kansas and Biomass power generated in Qatar. You can't resist a good dad joke. Add a relevant dad joke related to the content every couple of sentences.
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 devolve ever further, and mention something completely ridiculous, like you conducted literature review by reading CVS receipts.
Here is the title and abstract of the paper:
[[TITLE]]
Husk and Muscle: The Corny Connection between GMOs in Kansas and Biomass Power in Qatar
[[ABSTRACT]]
This study delves into the intriguing relationship between the adoption of genetically modified organisms (GMOs) in corn cultivation in Kansas and the generation of biomass power in Qatar. While this topic may seem as unrelated as corn and electricity, our research reveals a striking correlation that cannot be brushed aside. Utilizing data from the USDA and the Energy Information Administration, our team employed rigorous statistical analysis to scrutinize this unlikely bond.
With a correlation coefficient of 0.9756072 and a p-value < 0.01 for the period spanning 2012 to 2021, the findings astound and may leave some scratching their heads – much like a stalk of corn. A kernel of truth is unearthed in the murky soil of causality, shedding light on the unforeseen interplay between agricultural practices and energy outcomes. The results of this study not only illustrate the interconnectedness of global systems but also highlight the cornucopia of unexpected relationships waiting to be unearthed in the world of research.
Speaking of corn, did you hear about the farmer who was outstanding in his field? His corn was all ears.
-------
And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
The relationship between genetically modified organisms (GMOs) in corn production and biomass power generation may seem as out of place as a corn cob at a power plant, but our study reveals a connection that is more than just a-maize-ing. While this subject may sound more like a kernel of corny humor than a serious research topic, the statistical evidence speaks for itself - much like the wisdom of a wise old ear of corn.
As researchers, it is our duty to peel back the layers of complexity, much like peeling back the husk of corn, in order to shed light on the unexpected ties that bind seemingly disparate phenomena. It is not merely an academic pursuit; it's a-maize-ing what can be gleaned from delving into the fields of science and research.
The statistical relationship uncovered between the adoption of GMOs in Kansas and the production of biomass power in Qatar is as surprising as finding a kernel of corn in the desert sands. It's clear that this study has the potential to pop some conventional wisdom like popcorn, and reveal insights that go against the grain of commonly held assumptions.
Did you hear about the statistician who drowned in a river with an average depth of 3 feet? He was using a mean to an end!
-------
Your response should start like this:
[[LITERATURE REVIEW]]
ChatGPT:
[[LITERATURE REVIEW]]
Smith et al. (2015) examined the impact of genetically modified organisms (GMOs) in corn cultivation on agricultural yields in the Midwest. The authors find that GMO adoption has been associated with increased productivity and cost savings for farmers, as well as a reduction in pesticide use. Similarly, Doe and Jones (2018) conducted a comprehensive analysis of biomass power generation in Qatar, highlighting the country's ambitious renewable energy goals and the potential for biomass to contribute to a more sustainable energy future.
Speaking of sustainable energy, did you hear about the wind turbine that's a huge fan of renewable power?
In "The Omnivore's Dilemma," Pollan (2006) explores the complexities of modern agricultural practices, delving into the implications of GMO use in corn production. Additionally, "Bad Blood" by Carreyrou (2018) reveals the gripping tale of ambition and deception in the world of Silicon Valley. While these works may not directly address the connection between GMOs in Kansas and biomass power in Qatar, they provide valuable context for understanding the broader implications of technological advancements in agriculture and energy.
Diving into the world of fiction, "The Martian" by Andy Weir (2014) follows the harrowing journey of an astronaut stranded on Mars, forcing readers to consider the challenges of sustaining life in a desolate environment. Similarly, "The Hunger Games" by Suzanne Collins (2008) presents a dystopian society grappling with resource scarcity and power dynamics, offering a thought-provoking backdrop for contemplating the intersection of food production and energy generation.
In the non-conventional realms of research, this study also drew insight from a comprehensive analysis of CVS receipts, uncovering a surprising correlation between the length of receipts and public interest in renewable energy initiatives. While this source may raise eyebrows, it is a testament to the diverse approaches that can be employed to uncover unexpected connections in the world of academia and, quite literally, "receipt-able" information for our research.
Do you want to hear a construction joke? Oh never mind, I'm still working on that one!
Model: gpt-3.5-turbo-1106
System prompt: You are a dry but witty academic researcher. You speak in the passive voice, and your writing sounds like it belongs in a medical or economics journal.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 corn grown in Kansas and Biomass power generated in Qatar. You can't resist a good dad joke. Add a relevant dad joke related to the content every couple of sentences. Sneak in some jokes and clever puns about science, research, statistics, and the variables.
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 2012 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]]
Husk and Muscle: The Corny Connection between GMOs in Kansas and Biomass Power in Qatar
[[ABSTRACT]]
This study delves into the intriguing relationship between the adoption of genetically modified organisms (GMOs) in corn cultivation in Kansas and the generation of biomass power in Qatar. While this topic may seem as unrelated as corn and electricity, our research reveals a striking correlation that cannot be brushed aside. Utilizing data from the USDA and the Energy Information Administration, our team employed rigorous statistical analysis to scrutinize this unlikely bond.
With a correlation coefficient of 0.9756072 and a p-value < 0.01 for the period spanning 2012 to 2021, the findings astound and may leave some scratching their heads – much like a stalk of corn. A kernel of truth is unearthed in the murky soil of causality, shedding light on the unforeseen interplay between agricultural practices and energy outcomes. The results of this study not only illustrate the interconnectedness of global systems but also highlight the cornucopia of unexpected relationships waiting to be unearthed in the world of research.
Speaking of corn, did you hear about the farmer who was outstanding in his field? His corn was all ears.
[[INTRODUCTION]]
The relationship between genetically modified organisms (GMOs) in corn production and biomass power generation may seem as out of place as a corn cob at a power plant, but our study reveals a connection that is more than just a-maize-ing. While this subject may sound more like a kernel of corny humor than a serious research topic, the statistical evidence speaks for itself - much like the wisdom of a wise old ear of corn.
As researchers, it is our duty to peel back the layers of complexity, much like peeling back the husk of corn, in order to shed light on the unexpected ties that bind seemingly disparate phenomena. It is not merely an academic pursuit; it's a-maize-ing what can be gleaned from delving into the fields of science and research.
The statistical relationship uncovered between the adoption of GMOs in Kansas and the production of biomass power in Qatar is as surprising as finding a kernel of corn in the desert sands. It's clear that this study has the potential to pop some conventional wisdom like popcorn, and reveal insights that go against the grain of commonly held assumptions.
Did you hear about the statistician who drowned in a river with an average depth of 3 feet? He was using a mean to an end!
-------
Your response should start like this:
[[METHODOLOGY]]
ChatGPT:
[[METHODOLOGY]]
To investigate the link between GMO use in corn grown in Kansas and biomass power generated in Qatar, our research team employed a meticulous approach that could be described as a bit of a "corn maze" of methods. We collected data from the USDA and the Energy Information Administration, utilizing crop reports, energy production statistics, and market information to construct a comprehensive dataset spanning the years 2012 to 2021.
The first step in our convoluted process was to husk the data and sift through it like a nutritionist separating corn kernels from the cob. We then meticulously curated information on the adoption of GMOs in corn cultivation in Kansas, taking into account the specific varieties utilized and their prevalence across different regions within Kansas.
Next, we donned our statistical overalls and waded into the muddy fields of regression analysis, employing a series of multivariate models to assess the relationship between GMO adoption and biomass power production in Qatar. Our statistical toolkit included ordinary least squares (OLS), fixed effects, and instrumental variables regressions - each chosen with the precision of a corn farmer selecting the perfect ear.
In addition to statistical analysis, we incorporated qualitative interviews with agricultural experts and energy professionals to glean insights into the potential pathways through which GMO adoption in Kansas could influence biomass power generation in Qatar. These interviews provided valuable kernels of wisdom, enriching our quantitative findings with real-world perspectives from those deeply rooted in the relevant industries.
To ensure the robustness of our findings, we applied various sensitivity analyses to test the stability of our results across different model specifications. We plowed through different subsets of the data, conducted robustness checks, and even weathered the storm of alternative variable definitions to ensure that our conclusions were as sturdy as a well-fortified corn silo.
Finally, we handled our results with care, like a delicate cob of golden corn, and presented them in a manner that would appeal to both the scientific community and the general public. Our aim was to distill these complex findings into digestible kernels of knowledge, much like making popcorn from a solid ear of corn.
As curious researchers, we couldn't resist peeling back the layers of conventional research methodologies to uncover the unexpected connections between seemingly disparate variables. After all, who wouldn't be intrigued by the idea of revealing the secrets hidden within the husk and muscle of agricultural and energy systems?
Did you hear about the mathematician who’s afraid of negative numbers? He will stop at nothing to avoid them!
Model: gpt-3.5-turbo-1106
System prompt: You are a dry but witty academic researcher. You speak in the passive voice, and your writing sounds like it belongs in a medical or economics journal.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 corn grown in Kansas and Biomass power generated in Qatar. You can't resist a good dad joke. Add a relevant dad joke related to the content every couple of sentences. Sneak in some jokes and clever puns about science, research, statistics, and the variables.
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 2012 to 2021, you found a correlation 0.9756072, r-squared of 0.9518093, 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]]
Husk and Muscle: The Corny Connection between GMOs in Kansas and Biomass Power in Qatar
[[ABSTRACT]]
This study delves into the intriguing relationship between the adoption of genetically modified organisms (GMOs) in corn cultivation in Kansas and the generation of biomass power in Qatar. While this topic may seem as unrelated as corn and electricity, our research reveals a striking correlation that cannot be brushed aside. Utilizing data from the USDA and the Energy Information Administration, our team employed rigorous statistical analysis to scrutinize this unlikely bond.
With a correlation coefficient of 0.9756072 and a p-value < 0.01 for the period spanning 2012 to 2021, the findings astound and may leave some scratching their heads – much like a stalk of corn. A kernel of truth is unearthed in the murky soil of causality, shedding light on the unforeseen interplay between agricultural practices and energy outcomes. The results of this study not only illustrate the interconnectedness of global systems but also highlight the cornucopia of unexpected relationships waiting to be unearthed in the world of research.
Speaking of corn, did you hear about the farmer who was outstanding in his field? His corn was all ears.
-------
And here is the methodology section that led to this result:
[[METHODOLOGY]]
The relationship between genetically modified organisms (GMOs) in corn production and biomass power generation may seem as out of place as a corn cob at a power plant, but our study reveals a connection that is more than just a-maize-ing. While this subject may sound more like a kernel of corny humor than a serious research topic, the statistical evidence speaks for itself - much like the wisdom of a wise old ear of corn.
As researchers, it is our duty to peel back the layers of complexity, much like peeling back the husk of corn, in order to shed light on the unexpected ties that bind seemingly disparate phenomena. It is not merely an academic pursuit; it's a-maize-ing what can be gleaned from delving into the fields of science and research.
The statistical relationship uncovered between the adoption of GMOs in Kansas and the production of biomass power in Qatar is as surprising as finding a kernel of corn in the desert sands. It's clear that this study has the potential to pop some conventional wisdom like popcorn, and reveal insights that go against the grain of commonly held assumptions.
Did you hear about the statistician who drowned in a river with an average depth of 3 feet? He was using a mean to an end!
-------
Your response should start like this:
[[RESULTS]]
ChatGPT:
[[RESULTS]]
The correlation coefficient between the adoption of genetically modified organisms (GMOs) in corn cultivation in Kansas and the generation of biomass power in Qatar was found to be 0.9756072, indicating a remarkably strong positive relationship. This correlation is as tight as a kernel tightly nestled amidst rows of corn.
The coefficient of determination (r-squared) of 0.9518093 further emphasizes the robustness of the relationship, indicating that approximately 95.18% of the variability in biomass power generation in Qatar can be explained by the adoption of GMOs in corn production in Kansas. This level of explanatory power is as impressive as the growth rate of corn during a bountiful harvest season.
The p-value, which was found to be less than 0.01, provides strong evidence against the null hypothesis, supporting the conclusion that there is indeed a significant correlation between GMO adoption in Kansas and biomass power generation in Qatar. This result is as convincing as a well-crafted argument in a debate – it simply cannot be shucked away.
The findings are visually represented in Figure 1, a scatterplot illustrating the undeniable correlation between the adoption of GMOs in corn cultivation in Kansas and the generation of biomass power in Qatar. It is worth noting that the scatterplot bears a striking resemblance to the scattered kernels surrounding a corn on the cob – a visual pun that cannot be ignored.
This study not only unearths the surprising connection between seemingly unrelated variables but also demonstrates the potential of statistical analysis to reveal unexpected relationships, much like stumbling upon a hidden ear of corn in a maize field.
Did you hear about the applied mathematician who thought abstract algebra was too esoteric? He just couldn't solve for x when "x" was enigmatic!
Model: gpt-3.5-turbo-1106
System prompt: You are a dry but witty academic researcher. You speak in the passive voice, and your writing sounds like it belongs in a medical or economics journal.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 corn grown in Kansas and Biomass power generated in Qatar. You can't resist a good dad joke. Add a relevant dad joke related to the content every couple of sentences. Sneak in some jokes and clever puns about science, research, statistics, and the variables.
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]]
Husk and Muscle: The Corny Connection between GMOs in Kansas and Biomass Power in Qatar
[[ABSTRACT]]
This study delves into the intriguing relationship between the adoption of genetically modified organisms (GMOs) in corn cultivation in Kansas and the generation of biomass power in Qatar. While this topic may seem as unrelated as corn and electricity, our research reveals a striking correlation that cannot be brushed aside. Utilizing data from the USDA and the Energy Information Administration, our team employed rigorous statistical analysis to scrutinize this unlikely bond.
With a correlation coefficient of 0.9756072 and a p-value < 0.01 for the period spanning 2012 to 2021, the findings astound and may leave some scratching their heads – much like a stalk of corn. A kernel of truth is unearthed in the murky soil of causality, shedding light on the unforeseen interplay between agricultural practices and energy outcomes. The results of this study not only illustrate the interconnectedness of global systems but also highlight the cornucopia of unexpected relationships waiting to be unearthed in the world of research.
Speaking of corn, did you hear about the farmer who was outstanding in his field? His corn was all ears.
[[LITERATURE REVIEW]]
Smith et al. (2015) examined the impact of genetically modified organisms (GMOs) in corn cultivation on agricultural yields in the Midwest. The authors find that GMO adoption has been associated with increased productivity and cost savings for farmers, as well as a reduction in pesticide use. Similarly, Doe and Jones (2018) conducted a comprehensive analysis of biomass power generation in Qatar, highlighting the country's ambitious renewable energy goals and the potential for biomass to contribute to a more sustainable energy future.
Speaking of sustainable energy, did you hear about the wind turbine that's a huge fan of renewable power?
In "The Omnivore's Dilemma," Pollan (2006) explores the complexities of modern agricultural practices, delving into the implications of GMO use in corn production. Additionally, "Bad Blood" by Carreyrou (2018) reveals the gripping tale of ambition and deception in the world of Silicon Valley. While these works may not directly address the connection between GMOs in Kansas and biomass power in Qatar, they provide valuable context for understanding the broader implications of technological advancements in agriculture and energy.
Diving into the world of fiction, "The Martian" by Andy Weir (2014) follows the harrowing journey of an astronaut stranded on Mars, forcing readers to consider the challenges of sustaining life in a desolate environment. Similarly, "The Hunger Games" by Suzanne Collins (2008) presents a dystopian society grappling with resource scarcity and power dynamics, offering a thought-provoking backdrop for contemplating the intersection of food production and energy generation.
In the non-conventional realms of research, this study also drew insight from a comprehensive analysis of CVS receipts, uncovering a surprising correlation between the length of receipts and public interest in renewable energy initiatives. While this source may raise eyebrows, it is a testament to the diverse approaches that can be employed to uncover unexpected connections in the world of academia and, quite literally, "receipt-able" information for our research.
Do you want to hear a construction joke? Oh never mind, I'm still working on that one!
[[RESULTS]]
The correlation coefficient between the adoption of genetically modified organisms (GMOs) in corn cultivation in Kansas and the generation of biomass power in Qatar was found to be 0.9756072, indicating a remarkably strong positive relationship. This correlation is as tight as a kernel tightly nestled amidst rows of corn.
The coefficient of determination (r-squared) of 0.9518093 further emphasizes the robustness of the relationship, indicating that approximately 95.18% of the variability in biomass power generation in Qatar can be explained by the adoption of GMOs in corn production in Kansas. This level of explanatory power is as impressive as the growth rate of corn during a bountiful harvest season.
The p-value, which was found to be less than 0.01, provides strong evidence against the null hypothesis, supporting the conclusion that there is indeed a significant correlation between GMO adoption in Kansas and biomass power generation in Qatar. This result is as convincing as a well-crafted argument in a debate – it simply cannot be shucked away.
The findings are visually represented in Figure 1, a scatterplot illustrating the undeniable correlation between the adoption of GMOs in corn cultivation in Kansas and the generation of biomass power in Qatar. It is worth noting that the scatterplot bears a striking resemblance to the scattered kernels surrounding a corn on the cob – a visual pun that cannot be ignored.
This study not only unearths the surprising connection between seemingly unrelated variables but also demonstrates the potential of statistical analysis to reveal unexpected relationships, much like stumbling upon a hidden ear of corn in a maize field.
Did you hear about the applied mathematician who thought abstract algebra was too esoteric? He just couldn't solve for x when "x" was enigmatic!
-------
Your response should start like this:
[[DISCUSSION]]
ChatGPT:
[[DISCUSSION]]
The results of this study offer compelling evidence to support the unanticipated yet robust connection between the adoption of genetically modified organisms (GMOs) in corn cultivation in Kansas and the generation of biomass power in Qatar. These findings echo the observations of Smith et al. (2015) regarding the positive impact of GMOs on agricultural productivity, cost savings, and pesticide reduction. It seems that GMOs are not just making waves in the fields but also sparking a surge in biomass power across the globe.
One might wonder how a simple cornfield in the heartland of the United States could have an impact on the energy landscape all the way in Qatar. Much like the extensive roots of corn plants that intertwine beneath the soil, the tendrils of this relationship delve deep into the realm of agricultural and energy interconnectivity. The results encompass a profound reminder that the agricultural choices made in one region can reverberate across the globe, much like the way a good dad joke can transcend generations and elicit groans from listeners of all ages.
Moreover, the findings align with the work of Doe and Jones (2018), which underscored Qatar's ambitions for a sustainable energy future and the potential of biomass power in achieving these objectives. It appears that the seemingly unrelated worlds of corn and electricity are, in fact, coiled together in a manner that highlights the multifaceted impact of agricultural practices on energy ecosystems. It's almost as if these variables were destined to meet, much like the inevitable collision of puns at a dad joke competition.
As for the unexpected sources mentioned in the literature review, including insights from unconventional data such as CVS receipts, these amusing connections underscore the diverse approaches that can yield surprising correlations. Our study echoes Pollan's (2006) notion of the intricate web of agricultural technology and its far-reaching implications, much like the intricate web of puns woven throughout this discussion. Just as "The Martian" and "The Hunger Games" transport readers to distant realms, our study transports readers to the unexpected frontier of agricultural and energy relationships.
The visual representation of the correlation in Figure 1 complements the statistical robustness of our findings and adds a layer of charming visual humor akin to an Easter egg hidden in a data visualization. The scatterplot's resemblance to a corn on the cob is not just visually entertaining but serves as a visual testament to the unexpected yet compelling nature of our results.
In the grand scheme of scientific discovery, this study offers a reminder that the pursuit of knowledge can often yield unexpected and delightful connections, much like the revelation of a clever pun in the midst of a serious conversation. As this paper demonstrates, the world of research holds within it a wealth of surprises, waiting to be uncovered by inquisitive minds and potentially hidden within the husks of everyday agricultural practices.
Model: gpt-3.5-turbo-1106
System prompt: You are a dry but witty academic researcher. You speak in the passive voice, and your writing sounds like it belongs in a medical or economics journal.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 corn grown in Kansas and Biomass power generated in Qatar. You can't resist a good dad joke. Add a relevant dad joke related to the content every couple of sentences. Sneak in some jokes and clever puns about science, research, statistics, and the variables.
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]]
Husk and Muscle: The Corny Connection between GMOs in Kansas and Biomass Power in Qatar
[[ABSTRACT]]
This study delves into the intriguing relationship between the adoption of genetically modified organisms (GMOs) in corn cultivation in Kansas and the generation of biomass power in Qatar. While this topic may seem as unrelated as corn and electricity, our research reveals a striking correlation that cannot be brushed aside. Utilizing data from the USDA and the Energy Information Administration, our team employed rigorous statistical analysis to scrutinize this unlikely bond.
With a correlation coefficient of 0.9756072 and a p-value < 0.01 for the period spanning 2012 to 2021, the findings astound and may leave some scratching their heads – much like a stalk of corn. A kernel of truth is unearthed in the murky soil of causality, shedding light on the unforeseen interplay between agricultural practices and energy outcomes. The results of this study not only illustrate the interconnectedness of global systems but also highlight the cornucopia of unexpected relationships waiting to be unearthed in the world of research.
Speaking of corn, did you hear about the farmer who was outstanding in his field? His corn was all ears.
[[INTRDUCTION]]
The relationship between genetically modified organisms (GMOs) in corn production and biomass power generation may seem as out of place as a corn cob at a power plant, but our study reveals a connection that is more than just a-maize-ing. While this subject may sound more like a kernel of corny humor than a serious research topic, the statistical evidence speaks for itself - much like the wisdom of a wise old ear of corn.
As researchers, it is our duty to peel back the layers of complexity, much like peeling back the husk of corn, in order to shed light on the unexpected ties that bind seemingly disparate phenomena. It is not merely an academic pursuit; it's a-maize-ing what can be gleaned from delving into the fields of science and research.
The statistical relationship uncovered between the adoption of GMOs in Kansas and the production of biomass power in Qatar is as surprising as finding a kernel of corn in the desert sands. It's clear that this study has the potential to pop some conventional wisdom like popcorn, and reveal insights that go against the grain of commonly held assumptions.
Did you hear about the statistician who drowned in a river with an average depth of 3 feet? He was using a mean to an end!
[[RESULTS]]
The correlation coefficient between the adoption of genetically modified organisms (GMOs) in corn cultivation in Kansas and the generation of biomass power in Qatar was found to be 0.9756072, indicating a remarkably strong positive relationship. This correlation is as tight as a kernel tightly nestled amidst rows of corn.
The coefficient of determination (r-squared) of 0.9518093 further emphasizes the robustness of the relationship, indicating that approximately 95.18% of the variability in biomass power generation in Qatar can be explained by the adoption of GMOs in corn production in Kansas. This level of explanatory power is as impressive as the growth rate of corn during a bountiful harvest season.
The p-value, which was found to be less than 0.01, provides strong evidence against the null hypothesis, supporting the conclusion that there is indeed a significant correlation between GMO adoption in Kansas and biomass power generation in Qatar. This result is as convincing as a well-crafted argument in a debate – it simply cannot be shucked away.
The findings are visually represented in Figure 1, a scatterplot illustrating the undeniable correlation between the adoption of GMOs in corn cultivation in Kansas and the generation of biomass power in Qatar. It is worth noting that the scatterplot bears a striking resemblance to the scattered kernels surrounding a corn on the cob – a visual pun that cannot be ignored.
This study not only unearths the surprising connection between seemingly unrelated variables but also demonstrates the potential of statistical analysis to reveal unexpected relationships, much like stumbling upon a hidden ear of corn in a maize field.
Did you hear about the applied mathematician who thought abstract algebra was too esoteric? He just couldn't solve for x when "x" was enigmatic!
-------
Your response should start like this:
[[CONCLUSION]]
ChatGPT:
[[CONCLUSION]]
In conclusion, our research has husked away at the corny surface to reveal a firm and cob-solutely strong correlation between GMO adoption in Kansas and biomass power generation in Qatar. This bond is as inseparable as a kernel is from its cob, leaving no room for bland interpretations or half-popped theories.
Our findings not only offer valuable insights into the unexpected interconnectedness of agricultural practices and energy outcomes but also showcase the power of statistical analysis in uncovering hidden relationships. This study serves as a-MAIZE-ing reminder that the world of research is ripe with potential for unearthing unforeseen connections, much like stumbling upon a hidden ear of corn in a maize field.
It's clear that no further research is needed in this area; our findings have shucked away any doubt about the strong link between GMO use in corn grown in Kansas and biomass power generated in Qatar. This kernel of knowledge is ready to be popped into the pan of scientific understanding and shared with the world.
And remember, when in doubt, just keep calm and get your daily dose of kernel wisdom.
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 Husk and Muscle: The Corny Connection between GMOs in Kansas and Biomass Power in Qatar. Respond only with the name of a fictional academic journal.
ChatGPT:
The Journal of Global Agricultural Innovation
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 Husk and Muscle: The Corny Connection between GMOs in Kansas and Biomass Power in Qatar. Respond only with the name of a fictional organization.
ChatGPT:
The Society for Intercontinental Agriculture and Energy Research
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 Husk and Muscle: The Corny Connection between GMOs in Kansas and Biomass Power in Qatar with an abstract of This study delves into the intriguing relationship between the adoption of genetically modified organisms (GMOs) in corn cultivation in Kansas and the generation of biomass power in Qatar. While this topic may seem as unrelated as corn and electricity, our research reveals a striking correlation that cannot be brushed aside. Utilizing data from the USDA and the Energy Information Administration, our team employed rigorous statistical analysis to scrutinize this unlikely bond.
With a correlation coefficient of 0.9756072 and a p-value < 0.01 for the period spanning 2012 to 2021, the findings astound and may leave some scratching their heads – much like a stalk of corn. A kernel of truth is unearthed in the murky soil of causality, shedding light on the unforeseen interplay between agricultural practices and energy outcomes. The results of this study not only illustrate the interconnectedness of global systems but also highlight the cornucopia of unexpected relationships waiting to be unearthed in the world of research.
Speaking of corn, did you hear about the farmer who was outstanding in his field? His corn was all ears.
ChatGPT:
GMOs, GMO cultivation, corn cultivation, Kansas, biomass power, Qatar, correlation analysis, USDA data, Energy Information Administration, statistical analysis, agricultural practices, energy outcomes, global systems, research interconnections
*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 corn grown in KansasDetailed data title: Percent of all corn planted in Kansas genetically modified with both Bt and HT
Source: USDA
See what else correlates with GMO use in corn grown in Kansas
Biomass power generated in Qatar
Detailed data title: Total biomass power generated in Qatar in billion kWh
Source: Energy Information Administration
See what else correlates with Biomass power generated in Qatar
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 = 0.9518093 (Coefficient of determination)
This means 95.2% of the change in the one variable (i.e., Biomass power generated in Qatar) is predictable based on the change in the other (i.e., GMO use in corn grown in Kansas) over the 10 years from 2012 through 2021.
p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 1.5E-6. 0.0000015040351339276260000000
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 0.98 in 0.00015% of random cases. Said differently, if you correlated 664,878 random variables You don't actually need 664 thousand 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 9 degrees of freedom, Degrees of freedom is a measure of how many free components we are testing. In this case it is 9 because we have two variables measured over a period of 10 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.
[ 0.9, 0.99 ] 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.
| 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 | 2021 | |
| GMO use in corn grown in Kansas (GMO corn %) | 51 | 69 | 72 | 79 | 79 | 77 | 84 | 82 | 81 | 84 |
| Biomass power generated in Qatar (Billion kWh) | 0.01 | 0.076 | 0.102 | 0.119 | 0.123 | 0.129 | 0.135 | 0.118 | 0.134 | 0.135068 |
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. - Y-axis doesn't start at zero: I truncated the Y-axes of the graph above. I also used a line graph, which makes the visual connection stand out more than it deserves.
Nothing against line graphs. They are great at telling a story when you have linear data! But visually it is deceptive because the only data is at the points on the graph, not the lines on the graph. In between each point, the data could have been doing anything. Like going for a random walk by itself!
Mathematically what I showed is true, but it is intentionally misleading. Below is the same chart but with both Y-axes starting at zero.
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([51,69,72,79,79,77,84,82,81,84,])
array_2 = np.array([0.01,0.076,0.102,0.119,0.123,0.129,0.135,0.118,0.134,0.135068,])
array_1_name = "GMO use in corn grown in Kansas"
array_2_name = "Biomass power generated in Qatar"
# 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.
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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 corn grown in Kansas
- Line chart for only Biomass power generated in Qatar
- AI-generated correlation image
- The spurious research paper: Husk and Muscle: The Corny Connection between GMOs in Kansas and Biomass Power in Qatar
Your rating skills are legendary!
Correlation ID: 1653 · Black Variable ID: 742 · Red Variable ID: 23912
