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Spurious correlation #3,637 · View random

A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is Air pollution in Oxnard, California and the second variable is Kerosene used in Thailand.  The chart goes from 1980 to 2021, and the two variables track closely in value over that time. Small Image
Download png
, svg

AI explanation

As air pollution decreased in Oxnard, California, the local seagulls found it easier to breathe, allowing them to fly all the way to Thailand. These environmentally conscious seagulls then started a campaign promoting cleaner energy sources, which ultimately led to a decrease in kerosene usage in Thailand. It's a classic case of avian activism creating unexpected global connections!

Model: dalle-3
Prompt: Generate an image that looks like it is a scene from a Disney movie. Depict a picturesque park in Oxnard, California, where children play joyfully amid vibrant greenery under a serene, clear blue sky. In the distance, the smokestacks of factories emit significantly reduced pollution, allowing the sunlight to stream through. Meanwhile, in a small village in Thailand, visualize families gathered, happily cooking their evening meals over clean-burning stoves that no longer emit kerosene fumes, with a sense of tranquility and progress in the air. 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: Air pollution in Oxnard, California and Kerosene used in Thailand. Given these, which of the following image style descriptions seems most appropriate? Pick the one that seems to be related to one of the variables at hand. Please respond with only one of the following potential descriptions: An image that looks like it is a scene from A Disney Movie Sci-fi fantacy A comic-book-style illustration with muted colors and a dotted halftone pattern from the 1960s Magic realism Surrealism
ChatGPT: An image that looks like it is a scene from A Disney Movie

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 Air pollution in Oxnard, California and Kerosene used in Thailand, in the following situation: Because when Oxnard's air got cleaner, the pollution particles didn't hitch a ride on a passing jet to Thailand, leading to a decrease in kerosene fumes.. Keep the description simple.
ChatGPT: In Oxnard, California, a clear blue sky stretches overhead, with children playing in a park without a worry. The smokestacks of factories release less pollution into the air, allowing the sun to shine through clearly. Meanwhile, in a small village in Thailand, families cook their evening meals over clean-burning stoves, free from the smell of kerosene fumes that used to hang in the air.

Model: gpt-3.5-turbo-1106
System prompt: You write and improve image generation prompts for DALLE-3. You maintain the same subjects, but enhance the visuals.
User prompt: Please respond with an image generation prompt that would be likely to create an interesting visual using DALLE-3, using the following style: [ An image that looks like it is a scene from A Disney Movie ] and depicting this scene: In Oxnard, California, a clear blue sky stretches overhead, with children playing in a park without a worry. The smokestacks of factories release less pollution into the air, allowing the sun to shine through clearly. Meanwhile, in a small village in Thailand, families cook their evening meals over clean-burning stoves, free from the smell of kerosene fumes that used to hang in the air. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Generate an image that looks like it is a scene from a Disney movie. Depict a picturesque park in Oxnard, California, where children play joyfully amid vibrant greenery under a serene, clear blue sky. In the distance, the smokestacks of factories emit significantly reduced pollution, allowing the sunlight to stream through. Meanwhile, in a small village in Thailand, visualize families gathered, happily cooking their evening meals over clean-burning stoves that no longer emit kerosene fumes, with a sense of tranquility and progress in the air.

*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.

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 a decrease in Air pollution in Oxnard, California caused Kerosene used in Thailand to decrease. Make it feel plausible.

AI academic paper

(Because p < 0.01)
Connecting Kerosene Consumption in Thailand to Air Pollution in Oxnard, California: A Correlative Charade

The Journal of Environmental Absurdity

Jan 2024

Reminder: This paper is AI-generated. Not real!
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 witty academic researcher. Your writing sounds like it belongs in a statistics 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 Air pollution in Oxnard, California and Kerosene used in Thailand. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns.

Make the title an alliteration.

Your research team used data from Environmental Protection Agency and Energy Information Administration to assess this nagging question. You found a correlation coefficient of 0.9492154 and p < 0.01 for 1980 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]]
Connecting Kerosene Consumption in Thailand to Air Pollution in Oxnard, California: A Correlative Charade

[[ABSTRACT]]
This paper presents a statistical examination of the perplexing nexus between kerosene consumption in Thailand and air pollution in Oxnard, California. Data from the Environmental Protection Agency and the Energy Information Administration were meticulously scrutinized to calibrate the tendrils of this enigmatic web from 1980 to 2021. The findings boasted a staggering correlation coefficient of 0.9492154, amidst the fluttering uncertainty, with a p-value of less than 0.01. The results cast a pall of intrigue over the far-reaching influence of kerosene usage, wafting across continents and culminating in the unexpected miasma of air pollution in Oxnard, California. The implications of this airy alliance are ripe for further investigation, inviting theorists and practitioners alike to unravel the intricate tapestry of global atmospheric interplay.


Model: gpt-3.5-turbo-1106
System prompt: You are a witty academic researcher. Your writing sounds like it belongs in a statistics 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 Air pollution in Oxnard, California and Kerosene used in Thailand. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns. Sneak in some jokes and clever puns about science, research, statistics, and the variables.

Here is the title and abstract of the paper:
[[TITLE]]
Connecting Kerosene Consumption in Thailand to Air Pollution in Oxnard, California: A Correlative Charade

[[ABSTRACT]]
This paper presents a statistical examination of the perplexing nexus between kerosene consumption in Thailand and air pollution in Oxnard, California. Data from the Environmental Protection Agency and the Energy Information Administration were meticulously scrutinized to calibrate the tendrils of this enigmatic web from 1980 to 2021. The findings boasted a staggering correlation coefficient of 0.9492154, amidst the fluttering uncertainty, with a p-value of less than 0.01. The results cast a pall of intrigue over the far-reaching influence of kerosene usage, wafting across continents and culminating in the unexpected miasma of air pollution in Oxnard, California. The implications of this airy alliance are ripe for further investigation, inviting theorists and practitioners alike to unravel the intricate tapestry of global atmospheric interplay.

-------

Your response should start like this:

[[INTRODUCTION]]



ChatGPT:
[[INTRODUCTION]]

In the ubiquitous dance of atmospheric interactions, the connection between seemingly disparate variables often unveils itself as a whimsical waltz of statistical intrigue. Our study embarks upon a peculiar puzzlement, pondering the potential correlation between kerosene consumption in Thailand and the ethereal tendrils of air pollution in Oxnard, California. At first glance, one might find this conjured correlation as elusive as a vapor in the wind, but as we delve into the data, the interplay between these factors unfurls with unexpected harmonious discord.

The intriguing link between kerosene consumption and air pollution beckons forth a rigorous statistical tango, inviting us to sway through the numbers and derive meaningful conclusions. The vibrant dataset, spanning over four decades, allows us to twirl through time and tease out the nuances of this enigmatic relationship. As we waltz through the statistical measures, an enchanting correlation coefficient of 0.9492154 materializes, sending ripples of astonishment through the academic ballroom. The p-value, with its understated grace, pirouettes beneath the 0.01 threshold, leaving us to ponder the significance of this cosmic statistical choreography.

With a nod to the whimsical serendipity of research, this study sheds light on the mysterious influence of kerosene consumption pirouetting across the globe and culminating in the subtle, yet unmistakable, mist of air pollution in Oxnard, California. The findings offer a glimpse into the promenade of atmospheric interplay, enticing both researchers and practitioners to partake in the mirthful unraveling of this statistical riddle. Therefore, with a twinkle in our scientific eyes, let us embark on this capricious quest to untangle the gossamer threads of this correlative charade, inviting curiosity to lead the way through this statistical masquerade.


Model: gpt-3.5-turbo-1106
System prompt: You are a witty academic researcher. Your writing sounds like it belongs in a statistics 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 Air pollution in Oxnard, California and Kerosene used in Thailand. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns.

Speak in the present tense for this section (the authors find...), as is common in academic research paper literature reviews. Name the sources in a format similar to this: In "Book," the authors find lorem and ipsum.

Make up the lorem and ipsum part, but make it sound related to the topic at hand.

Start by naming serious-sounding studies by authors like Smith, Doe, and Jones - but then quickly devolve. Name some real non-fiction books that would be related to the topic. Then name some real fiction books that sound like they could be related. Then devolve ever further, and mention something completely ridiculous, like you conducted literature review by reading the backs of shampoo bottles.

Here is the title and abstract of the paper:
[[TITLE]]
Connecting Kerosene Consumption in Thailand to Air Pollution in Oxnard, California: A Correlative Charade

[[ABSTRACT]]
This paper presents a statistical examination of the perplexing nexus between kerosene consumption in Thailand and air pollution in Oxnard, California. Data from the Environmental Protection Agency and the Energy Information Administration were meticulously scrutinized to calibrate the tendrils of this enigmatic web from 1980 to 2021. The findings boasted a staggering correlation coefficient of 0.9492154, amidst the fluttering uncertainty, with a p-value of less than 0.01. The results cast a pall of intrigue over the far-reaching influence of kerosene usage, wafting across continents and culminating in the unexpected miasma of air pollution in Oxnard, California. The implications of this airy alliance are ripe for further investigation, inviting theorists and practitioners alike to unravel the intricate tapestry of global atmospheric interplay.

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
In the ubiquitous dance of atmospheric interactions, the connection between seemingly disparate variables often unveils itself as a whimsical waltz of statistical intrigue. Our study embarks upon a peculiar puzzlement, pondering the potential correlation between kerosene consumption in Thailand and the ethereal tendrils of air pollution in Oxnard, California. At first glance, one might find this conjured correlation as elusive as a vapor in the wind, but as we delve into the data, the interplay between these factors unfurls with unexpected harmonious discord.
The intriguing link between kerosene consumption and air pollution beckons forth a rigorous statistical tango, inviting us to sway through the numbers and derive meaningful conclusions. The vibrant dataset, spanning over four decades, allows us to twirl through time and tease out the nuances of this enigmatic relationship. As we waltz through the statistical measures, an enchanting correlation coefficient of 0.9492154 materializes, sending ripples of astonishment through the academic ballroom. The p-value, with its understated grace, pirouettes beneath the 0.01 threshold, leaving us to ponder the significance of this cosmic statistical choreography.
With a nod to the whimsical serendipity of research, this study sheds light on the mysterious influence of kerosene consumption pirouetting across the globe and culminating in the subtle, yet unmistakable, mist of air pollution in Oxnard, California. The findings offer a glimpse into the promenade of atmospheric interplay, enticing both researchers and practitioners to partake in the mirthful unraveling of this statistical riddle. Therefore, with a twinkle in our scientific eyes, let us embark on this capricious quest to untangle the gossamer threads of this correlative charade, inviting curiosity to lead the way through this statistical masquerade.

-------

Your response should start like this:

[[LITERATURE REVIEW]]



ChatGPT:
[[LITERATURE REVIEW]]

The present study builds upon previous research examining the link between kerosene consumption and air pollution, where such investigations have primarily focused on localized effects within specific regions. Smith et al. (2015) extensively explored the impact of kerosene usage on indoor air quality in rural households in developing countries, revealing a stark contrast between the pungent presence of kerosene fumes and the pristine allure of fresh outdoor air. Meanwhile, Doe and Jones (2018) ventured into the labyrinth of atmospheric chemistry, uncovering the intricate mechanisms by which airborne pollutants whimsically tango with unsuspecting particles, creating a whimsical ballet of chemical reactions.

Delving further into the miasma of interconnected environmental phenomena, "Air Pollution and Its Effects" by Environmental Expert (2019) brings to light the vapid dance of pollutants in urban atmospheres, offering a comprehensive examination of the intricate interplay between vehicular emissions and industrial exhalations. Moreover, "The Enigma of Kerosene Consumption" by Energy Economist (2017) waltzes through the economic intricacies of kerosene usage, offering a nuanced account of its widespread influence across global landscapes, not unlike an itinerant minstrel weaving tales from distant lands.

Venturing into the fictional realm, "The Unseen Cloud" by A. Nimbus (2020) offers a whimsical allegory of ethereal connections, where elusive tendrils of fog bind distant realms in an enchanting dance of fate. Similarly, "The Kerosene Chronicles" by E. Ignite (2018) weaves a spellbinding tale of kerosene's clandestine odyssey, igniting imaginations with its swirling narrative of interconnected destinies.

In a departure from conventional sources, the authors also gleaned insights from an unconventional corpus of literature, including the back labels of shampoo bottles, where the tantalizing promises of "fresh, clean air" led to a surprising detour into the fragrance of statistical musings. The unexpected intersection of toiletry trivia and statistical scrutiny underscored the capricious nature of academic exploration, reminding us that even in the most unusual places, the tendrils of knowledge can manifest and tickle the senses.

As the voracious gaze of statistical inquiry expands to encompass an eclectic array of sources, the study presents a tantalizing medley of perspectives, inviting both mirth and method into the scholarly fold.


Model: gpt-3.5-turbo-1106
System prompt: You are a witty academic researcher. Your writing sounds like it belongs in a statistics 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 Air pollution in Oxnard, California and Kerosene used in Thailand. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns. 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 Environmental Protection Agency and Energy Information Administration . You used data from 1980 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]]
Connecting Kerosene Consumption in Thailand to Air Pollution in Oxnard, California: A Correlative Charade

[[ABSTRACT]]
This paper presents a statistical examination of the perplexing nexus between kerosene consumption in Thailand and air pollution in Oxnard, California. Data from the Environmental Protection Agency and the Energy Information Administration were meticulously scrutinized to calibrate the tendrils of this enigmatic web from 1980 to 2021. The findings boasted a staggering correlation coefficient of 0.9492154, amidst the fluttering uncertainty, with a p-value of less than 0.01. The results cast a pall of intrigue over the far-reaching influence of kerosene usage, wafting across continents and culminating in the unexpected miasma of air pollution in Oxnard, California. The implications of this airy alliance are ripe for further investigation, inviting theorists and practitioners alike to unravel the intricate tapestry of global atmospheric interplay.

[[INTRODUCTION]]
In the ubiquitous dance of atmospheric interactions, the connection between seemingly disparate variables often unveils itself as a whimsical waltz of statistical intrigue. Our study embarks upon a peculiar puzzlement, pondering the potential correlation between kerosene consumption in Thailand and the ethereal tendrils of air pollution in Oxnard, California. At first glance, one might find this conjured correlation as elusive as a vapor in the wind, but as we delve into the data, the interplay between these factors unfurls with unexpected harmonious discord.
The intriguing link between kerosene consumption and air pollution beckons forth a rigorous statistical tango, inviting us to sway through the numbers and derive meaningful conclusions. The vibrant dataset, spanning over four decades, allows us to twirl through time and tease out the nuances of this enigmatic relationship. As we waltz through the statistical measures, an enchanting correlation coefficient of 0.9492154 materializes, sending ripples of astonishment through the academic ballroom. The p-value, with its understated grace, pirouettes beneath the 0.01 threshold, leaving us to ponder the significance of this cosmic statistical choreography.
With a nod to the whimsical serendipity of research, this study sheds light on the mysterious influence of kerosene consumption pirouetting across the globe and culminating in the subtle, yet unmistakable, mist of air pollution in Oxnard, California. The findings offer a glimpse into the promenade of atmospheric interplay, enticing both researchers and practitioners to partake in the mirthful unraveling of this statistical riddle. Therefore, with a twinkle in our scientific eyes, let us embark on this capricious quest to untangle the gossamer threads of this correlative charade, inviting curiosity to lead the way through this statistical masquerade.

-------

Your response should start like this:

[[METHODOLOGY]]



ChatGPT:

[[METHODOLOGY]]

To unravel the whimsical waltz of statistical intrigue between kerosene consumption in Thailand and the ethereal tendrils of air pollution in Oxnard, we embarked on a statistical tango of methodological maneuvers. Our research team pirouetted through the data collected primarily from the venerable vaults of the Environmental Protection Agency and the Energy Information Administration, spanning the epoch from 1980 to 2021.

First and foremost, we engaged in a categorical contest of data collection, seizing information on kerosene consumption in Thailand with all the tenacity of an insatiable algorithmic appetite. The quantities and intricacies of kerosene usage were meticulously tabulated, encompassing a myriad of statistical sources and databases, akin to unraveling the layers of a particularly enigmatic statistical onion.

The next comedic opera in this statistical symphony involved the acquisition of air pollution data in Oxnard, California, where we scrutinized atmospheric intricacies with the gravitas of a statistical sleuth. Pollutants such as particulate matter, nitrogen dioxide, and ozone were probed for their captivating contributions to the gaseous intricacies of the Oxnard atmosphere. The capricious nature of atmospheric data collection certainly kept us on our toes, much like a statistical tightrope walker navigating the flux of environmental factors.

Furthermore, to illuminate the cosmic statistical choreography between these variables, we indulged in a saucy statistical tango known as correlation analysis. Armed with the trusty spear of Pearson’s correlation coefficient, we probed the interplay between kerosene consumption in Thailand and air pollution in Oxnard, California, with all the fervor of a statistical Don Quixote tilting at the windmills of uncertainty.

The methodological merrymaking reached its zenith with the invocation of the enigmatic p-value, a statistical talisman that danced serenely beneath the threshold of 0.01. This svelte significance level whispered secret statistical omens, guiding us through the statistical masquerade with a tantalizing promise of meaningful correlation.

In a convoluted twist of scientific fate, the statistical methodology employed in this study, while imbued with the gravitas of academic inquiry, also bore the whimsy of statistical serendipity, coaxing forth the lighthearted spirit of research into this correlative charade. Thus, with a nod to the prodigious and the preposterous, our methodological meanderings paved the way for the unveiling of the statistical caprice that underpins the connection between kerosene consumption in Thailand and the ethereal tendrils of air pollution in Oxnard, California.


Model: gpt-3.5-turbo-1106
System prompt: You are a witty academic researcher. Your writing sounds like it belongs in a statistics 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 Air pollution in Oxnard, California and Kerosene used in Thailand. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns. 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 Environmental Protection Agency and Energy Information Administration .

For the time period 1980 to 2021, you found a correlation 0.9492154, r-squared of 0.9010099, 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]]
Connecting Kerosene Consumption in Thailand to Air Pollution in Oxnard, California: A Correlative Charade

[[ABSTRACT]]
This paper presents a statistical examination of the perplexing nexus between kerosene consumption in Thailand and air pollution in Oxnard, California. Data from the Environmental Protection Agency and the Energy Information Administration were meticulously scrutinized to calibrate the tendrils of this enigmatic web from 1980 to 2021. The findings boasted a staggering correlation coefficient of 0.9492154, amidst the fluttering uncertainty, with a p-value of less than 0.01. The results cast a pall of intrigue over the far-reaching influence of kerosene usage, wafting across continents and culminating in the unexpected miasma of air pollution in Oxnard, California. The implications of this airy alliance are ripe for further investigation, inviting theorists and practitioners alike to unravel the intricate tapestry of global atmospheric interplay.

-------

And here is the methodology section that led to this result:
[[METHODOLOGY]]
In the ubiquitous dance of atmospheric interactions, the connection between seemingly disparate variables often unveils itself as a whimsical waltz of statistical intrigue. Our study embarks upon a peculiar puzzlement, pondering the potential correlation between kerosene consumption in Thailand and the ethereal tendrils of air pollution in Oxnard, California. At first glance, one might find this conjured correlation as elusive as a vapor in the wind, but as we delve into the data, the interplay between these factors unfurls with unexpected harmonious discord.
The intriguing link between kerosene consumption and air pollution beckons forth a rigorous statistical tango, inviting us to sway through the numbers and derive meaningful conclusions. The vibrant dataset, spanning over four decades, allows us to twirl through time and tease out the nuances of this enigmatic relationship. As we waltz through the statistical measures, an enchanting correlation coefficient of 0.9492154 materializes, sending ripples of astonishment through the academic ballroom. The p-value, with its understated grace, pirouettes beneath the 0.01 threshold, leaving us to ponder the significance of this cosmic statistical choreography.
With a nod to the whimsical serendipity of research, this study sheds light on the mysterious influence of kerosene consumption pirouetting across the globe and culminating in the subtle, yet unmistakable, mist of air pollution in Oxnard, California. The findings offer a glimpse into the promenade of atmospheric interplay, enticing both researchers and practitioners to partake in the mirthful unraveling of this statistical riddle. Therefore, with a twinkle in our scientific eyes, let us embark on this capricious quest to untangle the gossamer threads of this correlative charade, inviting curiosity to lead the way through this statistical masquerade.

-------

Your response should start like this:

[[RESULTS]]



ChatGPT:

[[RESULTS]]

The statistical analysis of the relationship between kerosene consumption in Thailand and air pollution in Oxnard, California has unveiled an intriguing connection that mesmerizes the scientific community. The correlation coefficient of 0.9492154, indicative of a strong positive linear relationship, has emerged as the uncanny centerpiece of this statistical charade. This correlation, along with an r-squared value of 0.9010099, highlights the captivating nature of the interplay between these seemingly distinct variables, leaving researchers in awe of the cosmic dance of statistical measures.

The scatterplot depicted in Figure 1 elegantly encapsulates the robust correlation, showcasing the mesmerizing alignment of these two variables. This visual representation serves as a captivating tableau, inviting observers to marvel at the enchanting statistical choreography orchestrated by the intertwined variables.

With a p-value of less than 0.01, the significance of this correlative revelation shrouds the academic sphere in a captivating mist of speculation and wonder. The statistical waltz of this investigation has showcased the unexpected intercontinental tango between kerosene consumption in Thailand and the ethereal fog of air pollution in Oxnard, California. As the results unfurl before us, the implications of this statistical masquerade beckon forth a world of possibilities, inspiring further exploration and unlocking the enigmatic secrets of this airy alliance.


Model: gpt-3.5-turbo-1106
System prompt: You are a witty academic researcher. Your writing sounds like it belongs in a statistics 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 Air pollution in Oxnard, California and Kerosene used in Thailand. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns. 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]]
Connecting Kerosene Consumption in Thailand to Air Pollution in Oxnard, California: A Correlative Charade

[[ABSTRACT]]
This paper presents a statistical examination of the perplexing nexus between kerosene consumption in Thailand and air pollution in Oxnard, California. Data from the Environmental Protection Agency and the Energy Information Administration were meticulously scrutinized to calibrate the tendrils of this enigmatic web from 1980 to 2021. The findings boasted a staggering correlation coefficient of 0.9492154, amidst the fluttering uncertainty, with a p-value of less than 0.01. The results cast a pall of intrigue over the far-reaching influence of kerosene usage, wafting across continents and culminating in the unexpected miasma of air pollution in Oxnard, California. The implications of this airy alliance are ripe for further investigation, inviting theorists and practitioners alike to unravel the intricate tapestry of global atmospheric interplay.

[[LITERATURE REVIEW]]
The present study builds upon previous research examining the link between kerosene consumption and air pollution, where such investigations have primarily focused on localized effects within specific regions. Smith et al. (2015) extensively explored the impact of kerosene usage on indoor air quality in rural households in developing countries, revealing a stark contrast between the pungent presence of kerosene fumes and the pristine allure of fresh outdoor air. Meanwhile, Doe and Jones (2018) ventured into the labyrinth of atmospheric chemistry, uncovering the intricate mechanisms by which airborne pollutants whimsically tango with unsuspecting particles, creating a whimsical ballet of chemical reactions.
Delving further into the miasma of interconnected environmental phenomena, "Air Pollution and Its Effects" by Environmental Expert (2019) brings to light the vapid dance of pollutants in urban atmospheres, offering a comprehensive examination of the intricate interplay between vehicular emissions and industrial exhalations. Moreover, "The Enigma of Kerosene Consumption" by Energy Economist (2017) waltzes through the economic intricacies of kerosene usage, offering a nuanced account of its widespread influence across global landscapes, not unlike an itinerant minstrel weaving tales from distant lands.
Venturing into the fictional realm, "The Unseen Cloud" by A. Nimbus (2020) offers a whimsical allegory of ethereal connections, where elusive tendrils of fog bind distant realms in an enchanting dance of fate. Similarly, "The Kerosene Chronicles" by E. Ignite (2018) weaves a spellbinding tale of kerosene's clandestine odyssey, igniting imaginations with its swirling narrative of interconnected destinies.
In a departure from conventional sources, the authors also gleaned insights from an unconventional corpus of literature, including the back labels of shampoo bottles, where the tantalizing promises of "fresh, clean air" led to a surprising detour into the fragrance of statistical musings. The unexpected intersection of toiletry trivia and statistical scrutiny underscored the capricious nature of academic exploration, reminding us that even in the most unusual places, the tendrils of knowledge can manifest and tickle the senses.
As the voracious gaze of statistical inquiry expands to encompass an eclectic array of sources, the study presents a tantalizing medley of perspectives, inviting both mirth and method into the scholarly fold.

[[RESULTS]]
The statistical analysis of the relationship between kerosene consumption in Thailand and air pollution in Oxnard, California has unveiled an intriguing connection that mesmerizes the scientific community. The correlation coefficient of 0.9492154, indicative of a strong positive linear relationship, has emerged as the uncanny centerpiece of this statistical charade. This correlation, along with an r-squared value of 0.9010099, highlights the captivating nature of the interplay between these seemingly distinct variables, leaving researchers in awe of the cosmic dance of statistical measures.
The scatterplot depicted in Figure 1 elegantly encapsulates the robust correlation, showcasing the mesmerizing alignment of these two variables. This visual representation serves as a captivating tableau, inviting observers to marvel at the enchanting statistical choreography orchestrated by the intertwined variables.
With a p-value of less than 0.01, the significance of this correlative revelation shrouds the academic sphere in a captivating mist of speculation and wonder. The statistical waltz of this investigation has showcased the unexpected intercontinental tango between kerosene consumption in Thailand and the ethereal fog of air pollution in Oxnard, California. As the results unfurl before us, the implications of this statistical masquerade beckon forth a world of possibilities, inspiring further exploration and unlocking the enigmatic secrets of this airy alliance.

-------

Your response should start like this:

[[DISCUSSION]]



ChatGPT:

[[DISCUSSION]]

The results of this study have uncovered a mesmerizing connection between kerosene consumption in Thailand and air pollution in Oxnard, California, shedding light on the previously unexplored cosmic dance of statistical measures across geographic boundaries. As we delve into the implications of these findings, it becomes increasingly apparent that the interplay between these variables transcends mere statistical correlation, delving into the ethereal realms of atmospheric interconnectivity.

Our results echo the findings of Smith et al. (2015) and Doe and Jones (2018), where the pungent allure of kerosene fumes in rural households mirrors the atmospheric ballet of pollutants in urban environments. This interweaving of literature with amusing anecdotes, and the less conventional inspiration from shampoo bottle labels underscores the capricious nature of statistical exploration, a testament to the whimsical tangibility of knowledge.

The staggering correlation coefficient, akin to a virtuoso performance in statistical symphony, reinforces the foundation laid by previous research in elucidating the intricate relationship between kerosene consumption and air pollution. The robust visual representation provided by the scatterplot is reminiscent of a captivating tableau, akin to an enthralling performance that leaves observers dazzled by the statistical choreography of the intertwined variables.

The significant p-value further accentuates the enigmatic nature of this intercontinental tango, inviting the scientific community to partake in a lighthearted waltz of speculation and wonder regarding the far-reaching influence of kerosene usage. In doing so, our findings set the stage for further investigation, inspiring a world of possibilities and unlocking the enigmatic secrets of this airy alliance.

In conclusion, our study not only substantiates the linkage between kerosene consumption in Thailand and air pollution in Oxnard, California, but also elevates the scientific discourse to a whimsical symphony of interconnected knowledge. As we bid adieu to the overt statistical charade, we are left with an enduring sense of marvel at the serendipitous dances of scientific inquiry.


Model: gpt-3.5-turbo-1106
System prompt: You are a witty academic researcher. Your writing sounds like it belongs in a statistics 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 Air pollution in Oxnard, California and Kerosene used in Thailand. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns. 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]]
Connecting Kerosene Consumption in Thailand to Air Pollution in Oxnard, California: A Correlative Charade

[[ABSTRACT]]
This paper presents a statistical examination of the perplexing nexus between kerosene consumption in Thailand and air pollution in Oxnard, California. Data from the Environmental Protection Agency and the Energy Information Administration were meticulously scrutinized to calibrate the tendrils of this enigmatic web from 1980 to 2021. The findings boasted a staggering correlation coefficient of 0.9492154, amidst the fluttering uncertainty, with a p-value of less than 0.01. The results cast a pall of intrigue over the far-reaching influence of kerosene usage, wafting across continents and culminating in the unexpected miasma of air pollution in Oxnard, California. The implications of this airy alliance are ripe for further investigation, inviting theorists and practitioners alike to unravel the intricate tapestry of global atmospheric interplay.

[[INTRDUCTION]]
In the ubiquitous dance of atmospheric interactions, the connection between seemingly disparate variables often unveils itself as a whimsical waltz of statistical intrigue. Our study embarks upon a peculiar puzzlement, pondering the potential correlation between kerosene consumption in Thailand and the ethereal tendrils of air pollution in Oxnard, California. At first glance, one might find this conjured correlation as elusive as a vapor in the wind, but as we delve into the data, the interplay between these factors unfurls with unexpected harmonious discord.
The intriguing link between kerosene consumption and air pollution beckons forth a rigorous statistical tango, inviting us to sway through the numbers and derive meaningful conclusions. The vibrant dataset, spanning over four decades, allows us to twirl through time and tease out the nuances of this enigmatic relationship. As we waltz through the statistical measures, an enchanting correlation coefficient of 0.9492154 materializes, sending ripples of astonishment through the academic ballroom. The p-value, with its understated grace, pirouettes beneath the 0.01 threshold, leaving us to ponder the significance of this cosmic statistical choreography.
With a nod to the whimsical serendipity of research, this study sheds light on the mysterious influence of kerosene consumption pirouetting across the globe and culminating in the subtle, yet unmistakable, mist of air pollution in Oxnard, California. The findings offer a glimpse into the promenade of atmospheric interplay, enticing both researchers and practitioners to partake in the mirthful unraveling of this statistical riddle. Therefore, with a twinkle in our scientific eyes, let us embark on this capricious quest to untangle the gossamer threads of this correlative charade, inviting curiosity to lead the way through this statistical masquerade.

[[RESULTS]]
The statistical analysis of the relationship between kerosene consumption in Thailand and air pollution in Oxnard, California has unveiled an intriguing connection that mesmerizes the scientific community. The correlation coefficient of 0.9492154, indicative of a strong positive linear relationship, has emerged as the uncanny centerpiece of this statistical charade. This correlation, along with an r-squared value of 0.9010099, highlights the captivating nature of the interplay between these seemingly distinct variables, leaving researchers in awe of the cosmic dance of statistical measures.
The scatterplot depicted in Figure 1 elegantly encapsulates the robust correlation, showcasing the mesmerizing alignment of these two variables. This visual representation serves as a captivating tableau, inviting observers to marvel at the enchanting statistical choreography orchestrated by the intertwined variables.
With a p-value of less than 0.01, the significance of this correlative revelation shrouds the academic sphere in a captivating mist of speculation and wonder. The statistical waltz of this investigation has showcased the unexpected intercontinental tango between kerosene consumption in Thailand and the ethereal fog of air pollution in Oxnard, California. As the results unfurl before us, the implications of this statistical masquerade beckon forth a world of possibilities, inspiring further exploration and unlocking the enigmatic secrets of this airy alliance.

-------

Your response should start like this:

[[CONCLUSION]]



ChatGPT:

[[CONCLUSION]]

As we wrap up this statistical soiree, the enthralling connection between kerosene consumption in Thailand and the ethereal tendrils of air pollution in Oxnard, California continues to captivate the academic dance floor. The staggering correlation coefficient of 0.9492154, akin to two partners seamlessly gliding in a statistical waltz, has left us spellbound by the intricate interplay of these variables. The r-squared value of 0.9010099 adds another layer of astonishment, akin to a well-executed spin in the realm of statistical phenomena.

With a p-value of less than 0.01, the significance of this correlation shrouds the research arena in a mist of speculation and wonder, comparable to a magician's flurried reveal. The scatterplot, a visual testament to this statistical dance, stands as a mesmerizing tableau, inviting observers to marvel at the enchanting choreography orchestrated by the intertwined variables.

In the spirit of scientific mirth, we dare say that this correlative charade between kerosene consumption and air pollution has unveiled a statistical magic show worthy of the grandest scientific arenas. However, in a final act of whimsy, we assert that no further research is needed in this domain, trusting that this statistical masquerade has already provided us with a most delightful spectacle, beckoning us to move on to uncover new enigmatic relationships in the scientific ballroom.


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 Connecting Kerosene Consumption in Thailand to Air Pollution in Oxnard, California: A Correlative Charade. Respond only with the name of a fictional academic journal.

ChatGPT:

The Journal of Environmental Absurdity


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 Connecting Kerosene Consumption in Thailand to Air Pollution in Oxnard, California: A Correlative Charade. Respond only with the name of a fictional organization.

ChatGPT:

The Institute for Transcontinental Air Quality Studies


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 Connecting Kerosene Consumption in Thailand to Air Pollution in Oxnard, California: A Correlative Charade with an abstract of This paper presents a statistical examination of the perplexing nexus between kerosene consumption in Thailand and air pollution in Oxnard, California. Data from the Environmental Protection Agency and the Energy Information Administration were meticulously scrutinized to calibrate the tendrils of this enigmatic web from 1980 to 2021. The findings boasted a staggering correlation coefficient of 0.9492154, amidst the fluttering uncertainty, with a p-value of less than 0.01. The results cast a pall of intrigue over the far-reaching influence of kerosene usage, wafting across continents and culminating in the unexpected miasma of air pollution in Oxnard, California. The implications of this airy alliance are ripe for further investigation, inviting theorists and practitioners alike to unravel the intricate tapestry of global atmospheric interplay.

ChatGPT:

kerosene consumption, air pollution, Thailand, Oxnard California, statistical analysis, correlation coefficient, Environmental Protection Agency, Energy Information Administration, global atmospheric interplay, atmospheric pollution, environmental impact, transcontinental pollution, kerosene usage, atmospheric influence

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



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Data details

Air pollution in Oxnard, California
Detailed data title: Percentage of days with air quality at 'unhealthy for sensitive groups' or worse in Oxnard-Thousand Oaks-Ventura, CA
Source: Environmental Protection Agency
See what else correlates with Air pollution in Oxnard, California

Kerosene used in Thailand
Detailed data title: Volume of kerosene used consumed in Thailand in millions of barrels per day
Source: Energy Information Administration
See what else correlates with Kerosene used in Thailand

Correlation r = 0.9492154 (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 = 0.9010099 (Coefficient of determination)
This means 90.1% of the change in the one variable (i.e., Kerosene used in Thailand) is predictable based on the change in the other (i.e., Air pollution in Oxnard, California) over the 42 years from 1980 through 2021.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 1.08E-21. 0.0000000000000000000010753170
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 0.95 in 1.08E-19% of random cases. Said differently, if you correlated 929,958,328,567,296,819,200 random variables You don't actually need 929 quintillion 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 41 degrees of freedom, Degrees of freedom is a measure of how many free components we are testing. In this case it is 41 because we have two variables measured over a period of 42 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, 0.97 ] 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.
198019811982198319841985198619871988198919901991199219931994199519961997199819992000200120022003200420052006200720082009201020112012201320142015201620172018201920202021
Air pollution in Oxnard, California (Bad air quality days)44.109642.541447.945242.465842.896243.013750.13743.835648.087443.013738.082243.835633.333325.205535.342535.068534.972731.780820.821922.739722.950823.013718.082226.301421.857916.986314.246611.780815.573810.95894.931514.383568.743173.01374.109593.561642.459029.04114.657532.465757.103832.73973
Kerosene used in Thailand (Million Barrels/Day)2.294492.192062.038412.192062.386682.304742.42.222.12.11.921.921.71.71.510.90.8402930.9752741.392260.6168690.4003850.367020.339450.3136040.2697870.3032650.2636340.2240030.2302640.1895410.1860950.1826480.1890220.1323160.1326790.1303560.1116950.103386




Why this works

  1. 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.
  2. 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.
  3. Observations not independent: For many variables, sequential years are not independent of each other. If a population of people is continuously doing something every day, there is no reason to think they would suddenly change how they are doing that thing on January 1. A simple Personally I don't find any p-value calculation to be 'simple,' but you know what I mean.
    p-value calculation does not take this into account, so mathematically it appears less probable than it really is.




Try it yourself

You can calculate the values on this page on your own! Try running the Python code to see the calculation results. Step 1: Download and install Python on your computer.

Step 2: Open a plaintext editor like Notepad and paste the code below into it.

Step 3: Save the file as "calculate_correlation.py" in a place you will remember, like your desktop. Copy the file location to your clipboard. On Windows, you can right-click the file and click "Properties," and then copy what comes after "Location:" As an example, on my computer the location is "C:\Users\tyler\Desktop"

Step 4: Open a command line window. For example, by pressing start and typing "cmd" and them pressing enter.

Step 5: Install the required modules by typing "pip install numpy", then pressing enter, then typing "pip install scipy", then pressing enter.

Step 6: Navigate to the location where you saved the Python file by using the "cd" command. For example, I would type "cd C:\Users\tyler\Desktop" and push enter.

Step 7: Run the Python script by typing "python calculate_correlation.py"

If you run into any issues, I suggest asking ChatGPT to walk you through installing Python and running the code below on your system. Try this question:

"Walk me through installing Python on my computer to run a script that uses scipy and numpy. Go step-by-step and ask me to confirm before moving on. Start by asking me questions about my operating system so that you know how to proceed. Assume I want the simplest installation with the latest version of Python and that I do not currently have any of the necessary elements installed. Remember to only give me one step per response and confirm I have done it before proceeding."


# These modules make it easier to perform the calculation
import numpy as np
from scipy import stats

# We'll define a function that we can call to return the correlation calculations
def calculate_correlation(array1, array2):

    # Calculate Pearson correlation coefficient and p-value
    correlation, p_value = stats.pearsonr(array1, array2)

    # Calculate R-squared as the square of the correlation coefficient
    r_squared = correlation**2

    return correlation, r_squared, p_value

# These are the arrays for the variables shown on this page, but you can modify them to be any two sets of numbers
array_1 = np.array([44.1096,42.5414,47.9452,42.4658,42.8962,43.0137,50.137,43.8356,48.0874,43.0137,38.0822,43.8356,33.3333,25.2055,35.3425,35.0685,34.9727,31.7808,20.8219,22.7397,22.9508,23.0137,18.0822,26.3014,21.8579,16.9863,14.2466,11.7808,15.5738,10.9589,4.93151,4.38356,8.74317,3.0137,4.10959,3.56164,2.45902,9.0411,4.65753,2.46575,7.10383,2.73973,])
array_2 = np.array([2.29449,2.19206,2.03841,2.19206,2.38668,2.30474,2.4,2.2,2,2.1,2.1,1.9,2,1.9,2,1.7,1.7,1.5,1,0.9,0.840293,0.975274,1.39226,0.616869,0.400385,0.36702,0.33945,0.313604,0.269787,0.303265,0.263634,0.224003,0.230264,0.189541,0.186095,0.182648,0.189022,0.132316,0.132679,0.130356,0.111695,0.103386,])
array_1_name = "Air pollution in Oxnard, California"
array_2_name = "Kerosene used in Thailand"

# 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)



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Correlation ID: 3637 · Black Variable ID: 21036 · Red Variable ID: 24984
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