Spurious correlation #2,605 · View random
A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is GMO use in soybeans in Iowa and the second variable is Geothermal power generated in Russia. The chart goes from 2000 to 2021, and the two variables track closely in value over that time.

A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is GMO use in soybeans in Iowa and the second variable is Geothermal power generated in Russia. The chart goes from 2000 to 2021, and the two variables track closely in value over that time.

Download png, svg

AI explanation

As the GMO soybeans in Iowa flourished, they unknowingly formed a vast underground network, inadvertently connecting with the extensive root systems of Russian dandelions. This unprecedented transcontinental plant communication created a synergy that boosted geothermal power production in Russia. Essentially, it was a case of soy-powered dandelion technology, proving once and for all that when life gives you GMO soybeans, you also get a whole new way to generate energy, brought to you by the collaborative efforts of international flora.

^{Show image generation prompt} Model: dalle-3
Prompt: "Generate an image of a high-tech scene depicting rows of GMO soybeans in an Iowa field emitting fart-like methane, with large tubes connecting to a geothermal power plant in Russia. The tubes are filled with the potent gas, creating an unexpected partnership between the soybeans and the geothermal plant." 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 Iowa and Geothermal power generated in Russia. 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: Psychedelic A scene from Skyrim Pop art Voxel art High-tech
ChatGPT: High-tech

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 Iowa and Geothermal power generated in Russia, in the following situation: The GMO soybeans in Iowa were so full of beans, they couldn't stop farting. These soybeans were producing more methane than a herd of cows on a chili cook-off. But instead of just stinking up the farm, the soybean toots were collected and transported through a series of tubes to Russia. The powerful greenhouse gas was then used to fuel a geothermal power plant, giving a whole new meaning to "renewable energy." In other words, those Iowa soybeans were literally fueling Russia's energy needs with their pungent emissions. It's a gas-powered partnership that no one saw coming, but hey, at least it's cutting down on their carbon footprint, one bean at a time.. Keep the description simple.
ChatGPT: In Iowa, rows of GMO soybeans stand tall in a field, each bean bursting with potential. But these beans seem to be full of more than just nutrients - they're letting out continuous fart-like emissions, filling the air with methane. Large tubes stretch across the ocean, connecting to a geothermal power plant in Russia. The tubes are filled with the potent gas, creating a curious and unexpected partnership between the soybeans and the geothermal plant.

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: [ High-tech ] and depicting this scene: In Iowa, rows of GMO soybeans stand tall in a field, each bean bursting with potential. But these beans seem to be full of more than just nutrients - they're letting out continuous fart-like emissions, filling the air with methane. Large tubes stretch across the ocean, connecting to a geothermal power plant in Russia. The tubes are filled with the potent gas, creating a curious and unexpected partnership between the soybeans and the geothermal plant. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: "Generate an image of a high-tech scene depicting rows of GMO soybeans in an Iowa field emitting fart-like methane, with large tubes connecting to a geothermal power plant in Russia. The tubes are filled with the potent gas, creating an unexpected partnership between the soybeans and the geothermal plant."

*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.
^{Show prompt for explanation} Model: gpt-3.5-turbo-1106 (fine-tuned)
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 Iowa caused Geothermal power generated in Russia to increase. Make it feel plausible.

AI academic paper

(Because p < 0.01)

Soylent Green and Clean Power: Unveiling the Soy-Ful Connection between GMO Soybeans in Iowa and Geothermal Energy in Russia

The Journal of Sustainable Soy Science

Jan 2024

Reminder: This paper is AI-generated. Not real!

^{Show prompt used to generate this paper} Quick note: This sequence is long and repetitive. That's how the prompt engineering works for this one. I manage the output in Python to format it into this PDF.

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 soybeans in Iowa and Geothermal power generated in Russia. Make lots of 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 a couple popular internet memes that are related to one of the topics.

Here is the title and abstract of the paper:
[[TITLE]]
Soylent Green and Clean Power: Unveiling the "Soy-Ful" Connection between GMO Soybeans in Iowa and Geothermal Energy in Russia

[[ABSTRACT]]
This study delves into the surprising correlation between the use of genetically modified organisms (GMO) in soybean cultivation in Iowa and the generation of geothermal power in Russia. Despite the geographical and agricultural disparities between these two regions, our findings reveal a remarkably strong statistical association between the adoption of GMO soybeans in Iowa and the production of geothermal energy in Russia. Utilizing data from the United States Department of Agriculture and the Energy Information Administration, we computed a correlation coefficient of 0.9602335 and significant p-value of less than 0.01 for the period spanning 2000 to 2021. This unexpected relationship between the agricultural practices in the American Midwest and the energy landscape in Eurasia presents a "soy-ful" mystery that warrants further investigation and speculation. The implications of these findings may extend beyond the realms of agriculture and energy, possibly paving the way for interdisciplinary discourse and collaborative research in the field of "GMO-thermal dynamics" and its impact on global sustainability.

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
The interconnectedness of global systems never fails to surprise; from the flapping of a butterfly's wings in Brazil causing a typhoon in the South China Sea to the adoption of GMO soybeans in Iowa influencing geothermal power generation in Russia, the world continues to reveal its intricate web of connections. Our study sets out to explore this intriguing correlation, recognizing the skepticism it may initially evoke, akin to the puzzlement one might feel upon discovering a soybean plant sprouting in the fiery depths of a geothermal vent.
The flourishing of genetically modified organisms (GMOs) in the heartland of America and the harnessing of geothermal energy in the expanse of the Russian landmass may seem like two entirely disparate phenomena, like the odd pairing of borscht and cornbread. Nevertheless, our investigation, devoid of prescriptive hypotheses, uncover the robust statistical link binding these seemingly unrelated occurrences, leading us to coin the term "GMO-thermal dynamics."
For many, the mention of GMO soybeans conjures up images of discernible but polarizing food labels and debates over bioengineering ethics. Meanwhile, geothermal power often resides in the periphery of discussions on renewable energy, overshadowed by its more photogenic siblings, solar and wind power. Yet, our inquiry into the hidden kinship between these two lands evokes the feeling of stumbling upon a meaningful relationship in a crowded room, much like witnessing a fortuitous encounter between a soybean and a geyser.
The enigmatic nature of this connection prompts us to examine the implications not only for the realms of agriculture and energy but also for the cross-pollination of ideas across disciplines. As we embark on this scholarly adventure, we are reminded of the sage advice to "follow the beans" and the intriguing tale they have to tell, much like the unexpected twists and turns in a convoluted plot. Thus, we invite our readers to join us in delving deeper into the "soy-ful" mystery of GMO soybeans in Iowa and geothermal energy in Russia, a tale that is at once agricultural, energetic, and undeniably unconventional.

-------

Your response should start like this:

[[LITERATURE REVIEW]]

ChatGPT:
[[LITERATURE REVIEW]]

The quest to unravel the enigmatic bond between GMO soybeans in Iowa and geothermal energy in Russia has led researchers down a labyrinth of scholarly investigations. Smith and Doe (2010) explored the agricultural landscape of Iowa, delving into the proliferation of genetically modified soybeans and their impact on crop yields. Meanwhile, Jones (2012) conducted in-depth analyses of geothermal power generation in Russia, shedding light on the utilization of Earth's internal heat for sustainable energy production.

As we traverse deeper into the scholarly realm, we encounter books that shed light on the intricate world of GMOs and renewable energy. "GMOs: A Wealth of Health or Stealth of Death" by Green (2015) provides a comprehensive overview of the controversies and complexities surrounding genetically modified organisms, offering food for thought on the ethical and environmental dimensions of GMO cultivation. On the other hand, "The Power Beneath: Geothermal Energy Unearthed" by Rock (2018) uncovers the geothermal potential of the Earth's crust, offering insights into the utilization of this renewable energy source.

Drawing inspiration from fiction, we find ourselves navigating through a myriad of literary works that, though not directly related to our topic, manage to evoke the essence of unexpected connections. "The Da Vinci Code" by Dan Brown rekindles our fascination with cryptic linkages and obscure mysteries, paralleling our pursuit of the "soy-ful" connection between Iowa and Russia. Similarly, "Cloud Atlas" by David Mitchell captivates with its interwoven narratives, echoing the complex interplay between GMO soybeans and geothermal power in our study.

In an attempt to infuse levity into our scholarly expedition, we pause to acknowledge the influence of popular internet memes on our discourse. The "Distracted Boyfriend" meme, with its uncanny ability to encapsulate the allure of alternative options amidst preexisting commitments, parallels the intrigue of exploring an unconventional correlation between agricultural and energy systems. Furthermore, the "This is fine" dog meme humorously encapsulates the simultaneous perplexity and nonchalance that accompanies unexpected discoveries, much like our own astonishment at the "soy-ful" revelation.

In synthesizing these diverse sources, we unearth a rich tapestry of scholarly, fictional, and popular narrative threads that, when woven together, manifest as a whimsical yet profound exploration of the "soy-ful" connection between the heartlands of soybean cultivation in Iowa and the geothermal expanse of Russia.

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 soybeans in Iowa and Geothermal power generated in Russia. Make lots of 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]]
Soylent Green and Clean Power: Unveiling the "Soy-Ful" Connection between GMO Soybeans in Iowa and Geothermal Energy in Russia

[[ABSTRACT]]
This study delves into the surprising correlation between the use of genetically modified organisms (GMO) in soybean cultivation in Iowa and the generation of geothermal power in Russia. Despite the geographical and agricultural disparities between these two regions, our findings reveal a remarkably strong statistical association between the adoption of GMO soybeans in Iowa and the production of geothermal energy in Russia. Utilizing data from the United States Department of Agriculture and the Energy Information Administration, we computed a correlation coefficient of 0.9602335 and significant p-value of less than 0.01 for the period spanning 2000 to 2021. This unexpected relationship between the agricultural practices in the American Midwest and the energy landscape in Eurasia presents a "soy-ful" mystery that warrants further investigation and speculation. The implications of these findings may extend beyond the realms of agriculture and energy, possibly paving the way for interdisciplinary discourse and collaborative research in the field of "GMO-thermal dynamics" and its impact on global sustainability.

[[INTRODUCTION]]
The interconnectedness of global systems never fails to surprise; from the flapping of a butterfly's wings in Brazil causing a typhoon in the South China Sea to the adoption of GMO soybeans in Iowa influencing geothermal power generation in Russia, the world continues to reveal its intricate web of connections. Our study sets out to explore this intriguing correlation, recognizing the skepticism it may initially evoke, akin to the puzzlement one might feel upon discovering a soybean plant sprouting in the fiery depths of a geothermal vent.
The flourishing of genetically modified organisms (GMOs) in the heartland of America and the harnessing of geothermal energy in the expanse of the Russian landmass may seem like two entirely disparate phenomena, like the odd pairing of borscht and cornbread. Nevertheless, our investigation, devoid of prescriptive hypotheses, uncover the robust statistical link binding these seemingly unrelated occurrences, leading us to coin the term "GMO-thermal dynamics."
For many, the mention of GMO soybeans conjures up images of discernible but polarizing food labels and debates over bioengineering ethics. Meanwhile, geothermal power often resides in the periphery of discussions on renewable energy, overshadowed by its more photogenic siblings, solar and wind power. Yet, our inquiry into the hidden kinship between these two lands evokes the feeling of stumbling upon a meaningful relationship in a crowded room, much like witnessing a fortuitous encounter between a soybean and a geyser.
The enigmatic nature of this connection prompts us to examine the implications not only for the realms of agriculture and energy but also for the cross-pollination of ideas across disciplines. As we embark on this scholarly adventure, we are reminded of the sage advice to "follow the beans" and the intriguing tale they have to tell, much like the unexpected twists and turns in a convoluted plot. Thus, we invite our readers to join us in delving deeper into the "soy-ful" mystery of GMO soybeans in Iowa and geothermal energy in Russia, a tale that is at once agricultural, energetic, and undeniably unconventional.

-------

Your response should start like this:

[[METHODOLOGY]]

ChatGPT:

[[METHODOLOGY]]

In order to unravel the enigma of the "soy-ful" connection between GMO soybeans in Iowa and geothermal energy in Russia, a multifaceted approach was utilized. Data on GMO soybean adoption in Iowa and geothermal power generation in Russia from 2000 to 2021 was collected from public databases, primarily sourced from the United States Department of Agriculture (USDA) and the Energy Information Administration. The data was carefully curated to capture the dynamism of agricultural practices and energy production over the studied timeframe.

To establish the prevalence of GMO soybean cultivation in Iowa, a compilation of annual acreage of GMO soybeans was synthesized, incorporating information on adoption rates, distribution across counties, and the interplay of biotechnological advances. The assessment of geothermal power generation in Russia involved the aggregation of annual geothermal energy production, considering regional variances and technological advancements in harnessing geothermal resources.

The correlation analysis entailed the application of statistical tools to elucidate the relationship between the adoption of GMO soybeans in Iowa and the generation of geothermal power in Russia. A comprehensive examination of the dataset was performed, accounting for potential confounding variables such as climate patterns, regulatory frameworks, and global market dynamics. The statistical analyses were carried out using reputable software, ensuring the robustness and accuracy of the results.

Moreover, to enhance the comprehensiveness of our inquiry, supplementary data on factors such as climate conditions, technological innovations, and agricultural policies was incorporated. This comprehensive approach aimed to capture the intricate interplay between agricultural practices and energy dynamics, akin to unraveling the convoluted plot of a captivating mystery novel.

In addition, sensitivity analyses were conducted to evaluate the robustness of the observed correlation, encompassing variations in temporal window, subregional considerations, and alternative metrics of GMO soybean adoption and geothermal energy production. These sensitivity analyses sought to ascertain the stability and consistency of the "soy-ful" connection, akin to probing the resilience of a captivating storyline under diverse narrative lenses.

Furthermore, qualitative assessments were woven into the fabric of our methodology, exploring the contextual nuances and idiosyncrasies of GMO soybean cultivation in Iowa and geothermal energy generation in Russia. This qualitative dimension served to enrich our understanding of the cultural, economic, and technological contexts, adding depth to the narrative of the "soy-ful" connection.

The rigorous integration of quantitative analyses, sensitivity assessments, and qualitative insights formed the cornerstone of our methodology, providing a comprehensive lens through which to unravel the unexpected correlation between GMO soybeans in Iowa and geothermal energy in Russia. This methodological approach, akin to dissecting a complex puzzle, allowed for a thorough exploration of the "soy-ful" mystery, unveiling the interconnected dynamics between agricultural innovation and sustainable energy production.

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 soybeans in Iowa and Geothermal power generated in Russia. Make lots of 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]]
Soylent Green and Clean Power: Unveiling the "Soy-Ful" Connection between GMO Soybeans in Iowa and Geothermal Energy in Russia

[[ABSTRACT]]
This study delves into the surprising correlation between the use of genetically modified organisms (GMO) in soybean cultivation in Iowa and the generation of geothermal power in Russia. Despite the geographical and agricultural disparities between these two regions, our findings reveal a remarkably strong statistical association between the adoption of GMO soybeans in Iowa and the production of geothermal energy in Russia. Utilizing data from the United States Department of Agriculture and the Energy Information Administration, we computed a correlation coefficient of 0.9602335 and significant p-value of less than 0.01 for the period spanning 2000 to 2021. This unexpected relationship between the agricultural practices in the American Midwest and the energy landscape in Eurasia presents a "soy-ful" mystery that warrants further investigation and speculation. The implications of these findings may extend beyond the realms of agriculture and energy, possibly paving the way for interdisciplinary discourse and collaborative research in the field of "GMO-thermal dynamics" and its impact on global sustainability.

[[LITERATURE REVIEW]]
The quest to unravel the enigmatic bond between GMO soybeans in Iowa and geothermal energy in Russia has led researchers down a labyrinth of scholarly investigations. Smith and Doe (2010) explored the agricultural landscape of Iowa, delving into the proliferation of genetically modified soybeans and their impact on crop yields. Meanwhile, Jones (2012) conducted in-depth analyses of geothermal power generation in Russia, shedding light on the utilization of Earth's internal heat for sustainable energy production.
As we traverse deeper into the scholarly realm, we encounter books that shed light on the intricate world of GMOs and renewable energy. "GMOs: A Wealth of Health or Stealth of Death" by Green (2015) provides a comprehensive overview of the controversies and complexities surrounding genetically modified organisms, offering food for thought on the ethical and environmental dimensions of GMO cultivation. On the other hand, "The Power Beneath: Geothermal Energy Unearthed" by Rock (2018) uncovers the geothermal potential of the Earth's crust, offering insights into the utilization of this renewable energy source.
Drawing inspiration from fiction, we find ourselves navigating through a myriad of literary works that, though not directly related to our topic, manage to evoke the essence of unexpected connections. "The Da Vinci Code" by Dan Brown rekindles our fascination with cryptic linkages and obscure mysteries, paralleling our pursuit of the "soy-ful" connection between Iowa and Russia. Similarly, "Cloud Atlas" by David Mitchell captivates with its interwoven narratives, echoing the complex interplay between GMO soybeans and geothermal power in our study.
In an attempt to infuse levity into our scholarly expedition, we pause to acknowledge the influence of popular internet memes on our discourse. The "Distracted Boyfriend" meme, with its uncanny ability to encapsulate the allure of alternative options amidst preexisting commitments, parallels the intrigue of exploring an unconventional correlation between agricultural and energy systems. Furthermore, the "This is fine" dog meme humorously encapsulates the simultaneous perplexity and nonchalance that accompanies unexpected discoveries, much like our own astonishment at the "soy-ful" revelation.
In synthesizing these diverse sources, we unearth a rich tapestry of scholarly, fictional, and popular narrative threads that, when woven together, manifest as a whimsical yet profound exploration of the "soy-ful" connection between the heartlands of soybean cultivation in Iowa and the geothermal expanse of Russia.

[[RESULTS]]
The analysis of the data obtained from the United States Department of Agriculture and the Energy Information Administration yielded a correlation coefficient of 0.9602335 and an r-squared value of 0.9220483 for the period between 2000 and 2021. The p-value of less than 0.01 indicates a highly significant relationship between the use of genetically modified organisms (GMO) in soybean cultivation in Iowa and the generation of geothermal power in Russia.
Figure 1 depicts a scatterplot illustrating the remarkably strong correlation between the adoption of GMO soybeans in Iowa and the production of geothermal energy in Russia, though it regrettably fails to capture the essence of a soybean whispering its secrets to a geyser in the Siberian wilderness.
The robust statistical association between these two seemingly unrelated variables raises eyebrows, akin to the perplexity one might experience when witnessing a field of genetically modified soybeans waving in harmony with the undulating terrain of a Russian geyser field. This curious coupling beckons exploration and sparks the imagination, much like stumbling upon a barn dance where soybeans and geysers twirl in an unexpected waltz.
The findings of this study unveil a mysterious and thought-provoking connection between seemingly disparate elements of the global ecosystem, reminding us that, much like a well-kept recipe for soy-infused borscht, there are hidden linkages waiting to be discovered beneath the surface of our interconnected world. These results prompt further inquiry into the implications of GMO-thermal dynamics, inviting scholars to ponder the broader impacts of this "soy-ful" relationship and its potential to revolutionize interdisciplinary discourse and collaborative research in agriculture, energy, and beyond.

-------

Your response should start like this:

[[DISCUSSION]]

ChatGPT:

[[DISCUSSION]]

The striking revelation of a robust statistical correlation between the use of genetically modified organisms (GMO) in soybean cultivation in Iowa and the generation of geothermal power in Russia warrants a thoughtful examination. These findings not only validate the prior research but also introduce an intriguing avenue for further inquiry into the interplay between agricultural practices and renewable energy sources.

Harking back to the literature review, the unexpected connection dredges up memories of fictional narratives and popular internet memes that parallel the astonishment and curiosity evoked by our discovery. While initially perceived as whimsical associations, these seemingly disparate cultural influences mirror the unexpected intertwining of GMO soybeans and geothermal energy in our study, reinforcing the notion that reality can often outshine fiction in its complexity.

The strong correlation coefficient and significant p-value affirm the compelling relationship between the adoption of GMO soybeans in Iowa and the production of geothermal energy in Russia, much like the harmonious blend of flavors in a well-made soy-infused borscht. This resonates with the discussions in the literature review, where the complexities and controversies surrounding GMOs and the untapped potential of geothermal energy were met with equal parts skepticism and fascination.

Moreover, the meticulous analysis of the data aligns with the scholarly investigations into the agricultural and energy landscapes, providing empirical support for the captivating connection between these two seemingly disparate domains. The statistical rigor employed in our study underscores the gravity of the "soy-ful" relationship, much like the sturdy branches supporting a vineyard of soybeans looming over the subterranean reservoirs of geothermal potential.

The evocative imagery captured by the scatterplot, while falling short of capturing the full whimsy of our findings, paints a vivid picture of the entwined fate of GMO soybeans and geothermal power. The visual representation of this correlation, though lacking in soybean whispers to geyser serenades, conveys a narrative of unexpected harmony between agricultural innovation and renewable energy production.

The implications of these findings extend beyond the realms of agriculture and energy, much like the reverberating impact of a well-timed pun in a somber discourse. The "soy-ful" connection between Iowa's soybean fields and Russia's geothermal expanse may serve as a catalyst for interdisciplinary discourse and collaborative research, akin to the transformative power of a well-told joke in breaking down scholarly barriers and fostering knowledge exchange.

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 soybeans in Iowa and Geothermal power generated in Russia. Make lots of 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]]
Soylent Green and Clean Power: Unveiling the "Soy-Ful" Connection between GMO Soybeans in Iowa and Geothermal Energy in Russia

[[ABSTRACT]]
This study delves into the surprising correlation between the use of genetically modified organisms (GMO) in soybean cultivation in Iowa and the generation of geothermal power in Russia. Despite the geographical and agricultural disparities between these two regions, our findings reveal a remarkably strong statistical association between the adoption of GMO soybeans in Iowa and the production of geothermal energy in Russia. Utilizing data from the United States Department of Agriculture and the Energy Information Administration, we computed a correlation coefficient of 0.9602335 and significant p-value of less than 0.01 for the period spanning 2000 to 2021. This unexpected relationship between the agricultural practices in the American Midwest and the energy landscape in Eurasia presents a "soy-ful" mystery that warrants further investigation and speculation. The implications of these findings may extend beyond the realms of agriculture and energy, possibly paving the way for interdisciplinary discourse and collaborative research in the field of "GMO-thermal dynamics" and its impact on global sustainability.

[[INTRDUCTION]]
The interconnectedness of global systems never fails to surprise; from the flapping of a butterfly's wings in Brazil causing a typhoon in the South China Sea to the adoption of GMO soybeans in Iowa influencing geothermal power generation in Russia, the world continues to reveal its intricate web of connections. Our study sets out to explore this intriguing correlation, recognizing the skepticism it may initially evoke, akin to the puzzlement one might feel upon discovering a soybean plant sprouting in the fiery depths of a geothermal vent.
The flourishing of genetically modified organisms (GMOs) in the heartland of America and the harnessing of geothermal energy in the expanse of the Russian landmass may seem like two entirely disparate phenomena, like the odd pairing of borscht and cornbread. Nevertheless, our investigation, devoid of prescriptive hypotheses, uncover the robust statistical link binding these seemingly unrelated occurrences, leading us to coin the term "GMO-thermal dynamics."
For many, the mention of GMO soybeans conjures up images of discernible but polarizing food labels and debates over bioengineering ethics. Meanwhile, geothermal power often resides in the periphery of discussions on renewable energy, overshadowed by its more photogenic siblings, solar and wind power. Yet, our inquiry into the hidden kinship between these two lands evokes the feeling of stumbling upon a meaningful relationship in a crowded room, much like witnessing a fortuitous encounter between a soybean and a geyser.
The enigmatic nature of this connection prompts us to examine the implications not only for the realms of agriculture and energy but also for the cross-pollination of ideas across disciplines. As we embark on this scholarly adventure, we are reminded of the sage advice to "follow the beans" and the intriguing tale they have to tell, much like the unexpected twists and turns in a convoluted plot. Thus, we invite our readers to join us in delving deeper into the "soy-ful" mystery of GMO soybeans in Iowa and geothermal energy in Russia, a tale that is at once agricultural, energetic, and undeniably unconventional.

[[RESULTS]]
The analysis of the data obtained from the United States Department of Agriculture and the Energy Information Administration yielded a correlation coefficient of 0.9602335 and an r-squared value of 0.9220483 for the period between 2000 and 2021. The p-value of less than 0.01 indicates a highly significant relationship between the use of genetically modified organisms (GMO) in soybean cultivation in Iowa and the generation of geothermal power in Russia.
Figure 1 depicts a scatterplot illustrating the remarkably strong correlation between the adoption of GMO soybeans in Iowa and the production of geothermal energy in Russia, though it regrettably fails to capture the essence of a soybean whispering its secrets to a geyser in the Siberian wilderness.
The robust statistical association between these two seemingly unrelated variables raises eyebrows, akin to the perplexity one might experience when witnessing a field of genetically modified soybeans waving in harmony with the undulating terrain of a Russian geyser field. This curious coupling beckons exploration and sparks the imagination, much like stumbling upon a barn dance where soybeans and geysers twirl in an unexpected waltz.
The findings of this study unveil a mysterious and thought-provoking connection between seemingly disparate elements of the global ecosystem, reminding us that, much like a well-kept recipe for soy-infused borscht, there are hidden linkages waiting to be discovered beneath the surface of our interconnected world. These results prompt further inquiry into the implications of GMO-thermal dynamics, inviting scholars to ponder the broader impacts of this "soy-ful" relationship and its potential to revolutionize interdisciplinary discourse and collaborative research in agriculture, energy, and beyond.

-------

Your response should start like this:

[[CONCLUSION]]

ChatGPT:

[[CONCLUSION]]

In conclusion, the "soy-ful" connection between GMO soybeans in Iowa and geothermal energy in Russia has been substantiated by the robust statistical analysis presented in this study. The eyebrow-raising correlation coefficient of 0.9602335 and r-squared value of 0.9220483, coupled with a p-value of less than 0.01, unmistakably highlight the unexpected bond between these seemingly disconnected phenomena.

This revelation may prompt one to ponder the unlikelihood of such a connection, akin to stumbling upon a geyser gushing soy milk or a soybean morphing into a geothermal well. It evokes the perplexing delight one experiences when finding a solitary kernel of corn nestled in a Russian matryoshka doll.

Our findings, illustrated by the scatterplot akin to a painting capturing the dance of soybeans and geyser fields, beckon further exploration and contemplation. This discovery, much like a Russian nesting doll, reveals layers of complexity and interconnectedness in our global ecosystem that warrant continued investigation.

But fret not, fellow researchers, for this scholarly quest has led us to the culmination of our inquiry. Therefore, we assert with confidence that no further research is needed in the "soy-ful" connection between GMO soybeans in Iowa and geothermal energy in Russia. This peculiar phenomenon has been thoroughly unraveled, and we have cracked the enigmatic soybean code, leaving no stone unturned in this unconventional quest. Thus, we bid adieu to this "soy-ful" mystery, until the next unexpected pairing unveils itself in the labyrinth of interconnected phenomena.

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 Soylent Green and Clean Power: Unveiling the Soy-Ful Connection between GMO Soybeans in Iowa and Geothermal Energy in Russia. Respond only with the name of a fictional academic journal.

ChatGPT:

The Journal of Sustainable Soy Science

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 Soylent Green and Clean Power: Unveiling the Soy-Ful Connection between GMO Soybeans in Iowa and Geothermal Energy in Russia. Respond only with the name of a fictional organization.

ChatGPT:

The Society for Sustainable Agriculture and Renewable Energy (SSARE)

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 Soylent Green and Clean Power: Unveiling the Soy-Ful Connection between GMO Soybeans in Iowa and Geothermal Energy in Russia with an abstract of This study delves into the surprising correlation between the use of genetically modified organisms (GMO) in soybean cultivation in Iowa and the generation of geothermal power in Russia. Despite the geographical and agricultural disparities between these two regions, our findings reveal a remarkably strong statistical association between the adoption of GMO soybeans in Iowa and the production of geothermal energy in Russia. Utilizing data from the United States Department of Agriculture and the Energy Information Administration, we computed a correlation coefficient of 0.9602335 and significant p-value of less than 0.01 for the period spanning 2000 to 2021. This unexpected relationship between the agricultural practices in the American Midwest and the energy landscape in Eurasia presents a "soy-ful" mystery that warrants further investigation and speculation. The implications of these findings may extend beyond the realms of agriculture and energy, possibly paving the way for interdisciplinary discourse and collaborative research in the field of "GMO-thermal dynamics" and its impact on global sustainability.

ChatGPT:

GMO soybeans, Iowa, geothermal energy, Russia, correlation, genetically modified organisms, soybean cultivation, agriculture, geothermal power, statistical association, United States Department of Agriculture, Energy Information Administration, correlation coefficient, p-value, agriculture practices, energy landscape, Eurasia, interdisciplinary discourse, global sustainability

*There is a bunch of Python happening behind the scenes to turn this prompt sequence into a PDF.

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Data details

GMO use in soybeans in Iowa
Detailed data title: Percent of soybeans planted in Iowa that are genetically modified
Source: USDA
See what else correlates with GMO use in soybeans in Iowa

Geothermal power generated in Russia
Detailed data title: Total geothermal power generated in Russia in billion kWh
Source: Energy Information Administration
See what else correlates with Geothermal power generated in Russia

Correlation r = 0.9602335 (Pearson correlation coefficient)
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 = 0.9220483 (Coefficient of determination)
This means 92.2% of the change in the one variable (i.e., Geothermal power generated in Russia) is predictable based on the change in the other (i.e., GMO use in soybeans in Iowa) over the 22 years from 2000 through 2021.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 1.5E-12.^Show 0.0000000000015143320514098527
The p-value is a measure of how probable it is that we would randomly find a result this extreme.^Note 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 0.96 in 1.5E-10% of random cases. Said differently, if you correlated 660,357,151,570 random variables^Note You don't actually need 660 billion 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, ^Note 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.

[ 0.91, 0.98 ] 95% correlation confidence interval (using the Fisher z-transformation)
^{Read more about the confidence interval} 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: ^Note 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 Iowa (GMO soybeans %)	59	73	75	84	89	91	91	94	95	94	96	97	97	93	97	96	97	94	95	94	93	97
Geothermal power generated in Russia (Billion kWh)	0.058	0.091	0.156	0.324	0.403	0.41	0.463	0.485	0.465	0.464	0.505	0.522	0.477	0.444	0.455	0.457	0.446	0.435	0.426	0.433	0.421	0.445

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^Note 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^Note 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. ^Note 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. ^{Show the steps to do this.} 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([59,73,75,84,89,91,91,94,95,94,96,97,97,93,97,96,97,94,95,94,93,97,])
array_2 = np.array([0.058,0.091,0.156,0.324,0.403,0.41,0.463,0.485,0.465,0.464,0.505,0.522,0.477,0.444,0.455,0.457,0.446,0.435,0.426,0.433,0.421,0.445,])
array_1_name = "GMO use in soybeans in Iowa"
array_2_name = "Geothermal power generated in Russia"

# 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. ^Note 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 ^Note 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 Iowa
Line chart for only Geothermal power generated in Russia
AI-generated correlation image
The spurious research paper: Soylent Green and Clean Power: Unveiling the Soy-Ful Connection between GMO Soybeans in Iowa and Geothermal Energy in Russia

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Correlation ID: 2605 · Black Variable ID: 832 · Red Variable ID: 23921


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