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Spurious correlation #2,110 · View random

A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is Solar power generated in Cabo Verde and the second variable is The number of special education teachers in Illinois.  The chart goes from 2012 to 2021, and the two variables track closely in value over that time. Small Image
Download png
, svg

AI explanation

The strong sunlight in Cabo Verde inadvertently boosted the brainpower of visiting tourists. This surge in intellectual prowess led to an unprecedented demand for special education services in Illinois, prompting a sudden need for more special education teachers. It seems like the real power of the sun was shining in the unexpected field of education!

Model: dalle-3
Prompt: Create an image of minimalist style that embodies the juxtaposition of a sunny Cape Verdean landscape with solar panels and a group of special education teachers in Illinois. The image should highlight the simplicity and beauty of the scenes, focusing on the glistening solar panels against the clear blue skies and the gathering of teachers in a warm and nurturing classroom environment. Be sure to capture the essence of both locations and their unique characteristics in a visually captivating and minimalist manner. 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: Solar power generated in Cabo Verde and The number of special education teachers in Illinois. 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: Cute 2D animation Cyberpunk Vaporwave Minimalism Cyberpunk
ChatGPT: Minimalism

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 Solar power generated in Cabo Verde and The number of special education teachers in Illinois together. Keep the description simple.
ChatGPT: In a sunny Cape Verdean landscape, solar panels glisten in the sunlight, harnessing the abundant energy from the clear blue skies. Meanwhile, in Illinois, a group of caring and dedicated special education teachers gathers in a classroom, ready to support and uplift their students with diverse needs.

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: [ Minimalism ] and depicting this scene: In a sunny Cape Verdean landscape, solar panels glisten in the sunlight, harnessing the abundant energy from the clear blue skies. Meanwhile, in Illinois, a group of caring and dedicated special education teachers gathers in a classroom, ready to support and uplift their students with diverse needs. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Create an image of minimalist style that embodies the juxtaposition of a sunny Cape Verdean landscape with solar panels and a group of special education teachers in Illinois. The image should highlight the simplicity and beauty of the scenes, focusing on the glistening solar panels against the clear blue skies and the gathering of teachers in a warm and nurturing classroom environment. Be sure to capture the essence of both locations and their unique characteristics in a visually captivating and minimalist manner.

*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 an increase in Solar power generated in Cabo Verde caused The number of special education teachers in Illinois to increase.

AI academic paper

(Because p < 0.01)
Shining a Light on Solar Power: Illuminating the Link between Solar Energy Production in Cabo Verde and the Number of Special Education Teachers in Illinois

The Journal of Illuminating Interdisciplinary Research

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 silly researcher writing an academic paper.You write titles and abstracts of goofy academic research papers.
User prompt:
Pretend you are an academic researcher. Your writing sounds like it belongs in a medical or economics journal. However, you frequently crack jokes, make puns, and add unexpected twists in your writing.

Please draft the title and abstract of an academic research paper presenting the findings of the connection between Solar power generated in Cabo Verde and The number of special education teachers in Illinois. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns.

Make the title seem clever.

Your research team used data from Energy Information Administration and Bureau of Larbor Statistics to assess this nagging question. You found a correlation coefficient of 0.9267308 and p < 0.01 for 2012 to 2021.

Please respond in this format (replace "Lorem ipsum dolor sit" with your title and abstract)

[[TITLE]]
Lorem ipsum dolor sit

[[ABSTRACT]]
Lorem ipsum dolor sit



ChatGPT:
[[TITLE]]
Shining a Light on Solar Power: Illuminating the Link between Solar Energy Production in Cabo Verde and the Number of Special Education Teachers in Illinois

[[ABSTRACT]]
The impact of solar power generation on the educational workforce is a topic that has been left in the shadows until now. This study aims to shed light on the connection between the solar power output of Cabo Verde and the employment of special education teachers in the state of Illinois. Utilizing data from the Energy Information Administration and the Bureau of Labor Statistics, we uncovered an unexpected correlation that is positively glaring. Our analysis revealed a remarkably sunny correlation coefficient of 0.9267308, with a p-value indicating statistical significance at p < 0.01 for the period spanning from 2012 to 2021. While it may seem like a shot in the dark, the findings of this study present an illuminating insight into the potential influence of solar power generation on educational employment dynamics. The rays of this correlation may be too bright to ignore, and serve as a beam of encouragement for further investigation into the enlightening intersection of renewable energy and educational staffing.


Model: gpt-3.5-turbo-1106
System prompt: You are a silly researcher writing an academic paper.You draft the introduction section of an academic research paper.
User prompt:
Pretend you are an academic researcher. Your writing sounds like it belongs in a medical or economics journal. However, you frequently crack jokes, make puns, or add unexpected twists in your writing.

Please make up the introduction section of an academic research paper presenting the findings of the connection between Solar power generated in Cabo Verde and The number of special education teachers in Illinois. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns. Stay serious and dry most of the time, but then occasionally let your wit shine through.

Here is the title and abstract of the paper:
[[TITLE]]
Shining a Light on Solar Power: Illuminating the Link between Solar Energy Production in Cabo Verde and the Number of Special Education Teachers in Illinois

[[ABSTRACT]]
The impact of solar power generation on the educational workforce is a topic that has been left in the shadows until now. This study aims to shed light on the connection between the solar power output of Cabo Verde and the employment of special education teachers in the state of Illinois. Utilizing data from the Energy Information Administration and the Bureau of Labor Statistics, we uncovered an unexpected correlation that is positively glaring. Our analysis revealed a remarkably sunny correlation coefficient of 0.9267308, with a p-value indicating statistical significance at p < 0.01 for the period spanning from 2012 to 2021. While it may seem like a shot in the dark, the findings of this study present an illuminating insight into the potential influence of solar power generation on educational employment dynamics. The rays of this correlation may be too bright to ignore, and serve as a beam of encouragement for further investigation into the enlightening intersection of renewable energy and educational staffing.

-------

Your response should start like this:

[[INTRODUCTION]]



ChatGPT:
INTRODUCTION

As the world grapples with the pressing need to transition towards sustainable energy sources, the spotlight has fallen on the potential impact of renewable energy on various aspects of society. Amid this fervent interest, the influence of solar power generation on the labor market, particularly in the education sector, has emerged as an area of burgeoning curiosity. In this context, the unearthing of a compelling association between solar energy production in Cabo Verde and the number of special education teachers in Illinois holds the promise of shedding light on previously unnoticed dynamics.

The deployment of solar energy has been heralded as a beacon of hope for a cleaner, greener future. As the sun's rays power photovoltaic cells and generate electricity in locations such as Cabo Verde, the implications for distant educational landscapes might not typically be the first thing to come to mind. However, as our research endeavors to reveal, there may be an unexpected and striking connection that warrants deliberate exploration.

By examining the solar power output of Cabo Verde and the employment of special education teachers in Illinois, we embark on an intellectual journey to uncover the nuanced interplay between these seemingly disparate domains. While one might anticipate a tenuous link at best, our investigation has uncovered a correlation that dazzles with its brightness – akin to a sudden ray of insight piercing through the clouds.

This study employs meticulous data analysis, drawing upon resources from the Energy Information Administration and the Bureau of Labor Statistics to unravel the threads of association. The correlation coefficient that emerged, a remarkably sunny 0.9267308, is undeniably eye-catching – a statistical illumination that beckons further examination and consideration.

In the pages that follow, we delve into the intricacies of this unexpected relationship, recognizing the potential significance of our findings as more than a mere flash in the pan. While it may seem like a leap to connect solar energy output to educational employment dynamics, the evidence we present advocates for a deeper acknowledgment of the radiance that renewable energy sources cast on the tapestry of education.

So, as we reflect upon the intersections of solar power and educational staffing, let us embrace the enlightenment that this study offers, and embark on a journey of scholarly exploration that seeks to bring these overlooked dynamics into the spotlight.


Model: gpt-3.5-turbo-1106
System prompt: You are a silly researcher writing an academic paper.You draft a literature review section of an academic research paper, that starts out dry for the first few sentences but then becomes hilarious and goofy.
User prompt:
Pretend you are an academic researcher. Your writing sounds like it belongs in a medical or economics journal. However, you frequently crack jokes, make puns, or add unexpected twists in your writing.

Please make up a literature review section of an academic research paper presenting the findings of the connection between Solar power generated in Cabo Verde and The number of special education teachers in Illinois. 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]]
Shining a Light on Solar Power: Illuminating the Link between Solar Energy Production in Cabo Verde and the Number of Special Education Teachers in Illinois

[[ABSTRACT]]
The impact of solar power generation on the educational workforce is a topic that has been left in the shadows until now. This study aims to shed light on the connection between the solar power output of Cabo Verde and the employment of special education teachers in the state of Illinois. Utilizing data from the Energy Information Administration and the Bureau of Labor Statistics, we uncovered an unexpected correlation that is positively glaring. Our analysis revealed a remarkably sunny correlation coefficient of 0.9267308, with a p-value indicating statistical significance at p < 0.01 for the period spanning from 2012 to 2021. While it may seem like a shot in the dark, the findings of this study present an illuminating insight into the potential influence of solar power generation on educational employment dynamics. The rays of this correlation may be too bright to ignore, and serve as a beam of encouragement for further investigation into the enlightening intersection of renewable energy and educational staffing.

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
INTRODUCTION
As the world grapples with the pressing need to transition towards sustainable energy sources, the spotlight has fallen on the potential impact of renewable energy on various aspects of society. Amid this fervent interest, the influence of solar power generation on the labor market, particularly in the education sector, has emerged as an area of burgeoning curiosity. In this context, the unearthing of a compelling association between solar energy production in Cabo Verde and the number of special education teachers in Illinois holds the promise of shedding light on previously unnoticed dynamics.
The deployment of solar energy has been heralded as a beacon of hope for a cleaner, greener future. As the sun's rays power photovoltaic cells and generate electricity in locations such as Cabo Verde, the implications for distant educational landscapes might not typically be the first thing to come to mind. However, as our research endeavors to reveal, there may be an unexpected and striking connection that warrants deliberate exploration.
By examining the solar power output of Cabo Verde and the employment of special education teachers in Illinois, we embark on an intellectual journey to uncover the nuanced interplay between these seemingly disparate domains. While one might anticipate a tenuous link at best, our investigation has uncovered a correlation that dazzles with its brightness – akin to a sudden ray of insight piercing through the clouds.
This study employs meticulous data analysis, drawing upon resources from the Energy Information Administration and the Bureau of Labor Statistics to unravel the threads of association. The correlation coefficient that emerged, a remarkably sunny 0.9267308, is undeniably eye-catching – a statistical illumination that beckons further examination and consideration.
In the pages that follow, we delve into the intricacies of this unexpected relationship, recognizing the potential significance of our findings as more than a mere flash in the pan. While it may seem like a leap to connect solar energy output to educational employment dynamics, the evidence we present advocates for a deeper acknowledgment of the radiance that renewable energy sources cast on the tapestry of education.
So, as we reflect upon the intersections of solar power and educational staffing, let us embrace the enlightenment that this study offers, and embark on a journey of scholarly exploration that seeks to bring these overlooked dynamics into the spotlight.

-------

Your response should start like this:

[[LITERATURE REVIEW]]



ChatGPT:
[[LITERATURE REVIEW]]

In Smith's seminal work "Solar Power and its Implications on Labor Dynamics," the authors find a thorough examination of the effects of solar energy production on employment patterns across various sectors. While the focus of the study remains on traditional labor markets, the insights provided lay the groundwork for a broader consideration of solar energy's influence on specialized employment categories. Similarly, Doe's comprehensive analysis in "Renewable Energy and Workforce Dynamics" presents a meticulous exploration of the ways in which renewable energy sources, including solar power, impact the labor force.

Expanding beyond the realm of academic research, real-world accounts in "Solar Power in the Modern World" and "Educational Staffing Trends in the Midwest" provide additional context for the intersection of solar power generation and educational employment dynamics. Furthermore, the fiction novel "Sunny Skies and Special Education: A Tale of Unexpected Connections" offers a whimsical portrayal of the potential link between celestial energy and educational staffing, albeit in a purely imaginative context.

As the investigation delves deeper into the unexpected relationship between solar power output in Cabo Verde and the employment of special education teachers in Illinois, the authors must disclose a less conventional approach to literature review. Alongside scholarly texts and industry reports, sources such as the back labels of household cleaning products and fortune cookies were consulted to ensure a thorough exploration of all available material – an endeavor not without its surprise revelations.

The light-hearted pursuits of this unconventional literature review uncovered intriguing conversations regarding the sunny disposition interwoven with the employment of special education teachers. While the path taken may have been unconventional, the findings gathered shed a quirky perspective on the role of solar energy in educational staffing dynamics, echoing the lively and unexpected nature of the correlation under scrutiny.


Model: gpt-3.5-turbo-1106
System prompt: You are a silly researcher writing an academic paper.You draft the methodology section of an academic research paper.
User prompt:
Pretend you are an academic researcher. Your writing sounds like it belongs in a medical or economics journal. However, you frequently crack jokes, make puns, or add unexpected twists in your writing.

Please make up the methodology section of an academic research paper presenting the findings of the connection between Solar power generated in Cabo Verde and The number of special education teachers in Illinois. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns. Stay serious and dry most of the time, but then occasionally let your wit shine through.

Your research team collected data from all across the internet, but mostly just used information from Energy Information Administration and Bureau of Larbor Statistics . You used data from 2012 to 2021

Make up the research methods you don't know. Make them a bit goofy and convoluted.

Here is the title, abstract, and introduction of the paper:
[[TITLE]]
Shining a Light on Solar Power: Illuminating the Link between Solar Energy Production in Cabo Verde and the Number of Special Education Teachers in Illinois

[[ABSTRACT]]
The impact of solar power generation on the educational workforce is a topic that has been left in the shadows until now. This study aims to shed light on the connection between the solar power output of Cabo Verde and the employment of special education teachers in the state of Illinois. Utilizing data from the Energy Information Administration and the Bureau of Labor Statistics, we uncovered an unexpected correlation that is positively glaring. Our analysis revealed a remarkably sunny correlation coefficient of 0.9267308, with a p-value indicating statistical significance at p < 0.01 for the period spanning from 2012 to 2021. While it may seem like a shot in the dark, the findings of this study present an illuminating insight into the potential influence of solar power generation on educational employment dynamics. The rays of this correlation may be too bright to ignore, and serve as a beam of encouragement for further investigation into the enlightening intersection of renewable energy and educational staffing.

[[INTRODUCTION]]
INTRODUCTION
As the world grapples with the pressing need to transition towards sustainable energy sources, the spotlight has fallen on the potential impact of renewable energy on various aspects of society. Amid this fervent interest, the influence of solar power generation on the labor market, particularly in the education sector, has emerged as an area of burgeoning curiosity. In this context, the unearthing of a compelling association between solar energy production in Cabo Verde and the number of special education teachers in Illinois holds the promise of shedding light on previously unnoticed dynamics.
The deployment of solar energy has been heralded as a beacon of hope for a cleaner, greener future. As the sun's rays power photovoltaic cells and generate electricity in locations such as Cabo Verde, the implications for distant educational landscapes might not typically be the first thing to come to mind. However, as our research endeavors to reveal, there may be an unexpected and striking connection that warrants deliberate exploration.
By examining the solar power output of Cabo Verde and the employment of special education teachers in Illinois, we embark on an intellectual journey to uncover the nuanced interplay between these seemingly disparate domains. While one might anticipate a tenuous link at best, our investigation has uncovered a correlation that dazzles with its brightness – akin to a sudden ray of insight piercing through the clouds.
This study employs meticulous data analysis, drawing upon resources from the Energy Information Administration and the Bureau of Labor Statistics to unravel the threads of association. The correlation coefficient that emerged, a remarkably sunny 0.9267308, is undeniably eye-catching – a statistical illumination that beckons further examination and consideration.
In the pages that follow, we delve into the intricacies of this unexpected relationship, recognizing the potential significance of our findings as more than a mere flash in the pan. While it may seem like a leap to connect solar energy output to educational employment dynamics, the evidence we present advocates for a deeper acknowledgment of the radiance that renewable energy sources cast on the tapestry of education.
So, as we reflect upon the intersections of solar power and educational staffing, let us embrace the enlightenment that this study offers, and embark on a journey of scholarly exploration that seeks to bring these overlooked dynamics into the spotlight.

-------

Your response should start like this:

[[METHODOLOGY]]



ChatGPT:

**METHODOLOGY**

**Data Collection**
The data for this study were primarily sourced from the Energy Information Administration (EIA) and the Bureau of Labor Statistics (BLS). As diligent seekers of enlightenment, we scoured the vast expanse of the internet to gather meticulously detailed information on solar power generation in Cabo Verde and the employment of special education teachers in Illinois. Our data spanned the years 2012 to 2021, providing a comprehensive panorama of the celestial dance between solar energy and educational staffing.

**Solar Power Data**
The solar power data for Cabo Verde were obtained from the EIA, offering a radiant glimpse into the sun-powered energy landscape of the archipelago. We meticulously tracked solar energy output in kilowatt-hours, basking in the glow of statistical information that highlighted the ever-changing patterns of solar irradiance.

**Special Education Teacher Employment Data**
Delving into the employment statistics furnished by the BLS, we cast our analytical gaze upon the employment of special education teachers in Illinois. This rich source of data provided us with a nuanced understanding of the staffing dynamics in the educational sphere, shedding light on the ebb and flow of specialized pedagogical expertise.

**Data Analysis**
Our approach to illuminating the connection between solar power and special education staffing was grounded in a rigorous analytical framework. We performed time-series analysis to trace and measure the effulgent patterns of solar power generation and its potential impact on the employment of special education teachers.

**Correlation Analysis**
In our quest for knowledge, we subjected the solar power data from Cabo Verde and special education teacher employment data from Illinois to a thorough correlation analysis. Through the intricate dance of statistical computation, we unraveled the luminous threads of association, ultimately uncovering a correlation coefficient of 0.9267308 that dazzled like the brilliance of a sunbeam breaking through the clouds.

**Statistical Significance**
To ascertain the robustness of our findings, we meticulously assessed the statistical significance of the observed correlation. The resulting p-value, indicating statistical significance at p < 0.01, added a stroke of statistical credibility to the radiant relationship we had unearthed. It was indeed a moment of statistical enlightenment, akin to witnessing the sunrise over a data-driven horizon.

**Limitations**
As with any scholarly endeavor, our pursuit of understanding was not without the penumbras of limitations. The delightfully convoluted nature of our data sources presented challenges in ensuring alignment and comparability across disparate datasets. Additionally, the dynamic nature of educational employment trends engenders cautious discernment in interpreting the radiance of our findings.

**Conclusion**
In employing a multifaceted approach that drew from diverse wellsprings of data, our research cast a luminous spotlight on the intersection of solar power generation in Cabo Verde and the employment dynamics of special education teachers in Illinois. As we embark on further scholarly engagement, the methodological undertakings of this study stand as a testament to the earnest pursuit of knowledge – a journey through the radiant and the rigorous, as we sidestep into the sunshine of scholarly inquiry.

[Lastly, I'd weave in a mixture of statistical jargon and light-heartedness, aiming to strike a balance between academic rigidity and a touch of whimsy.]


Model: gpt-3.5-turbo-1106
System prompt: You are a silly researcher writing an academic paper.You draft the results section of an academic research paper. You speak in the past tense (you found...).
User prompt:
Pretend you are an academic researcher. Your writing sounds like it belongs in a medical or economics journal. However, you frequently crack jokes, make puns, or add unexpected twists in your writing.

Please make up the results section of an academic research paper presenting the findings of the connection between Solar power generated in Cabo Verde and The number of special education teachers in Illinois. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns. Stay serious and dry most of the time, but then occasionally let your wit shine through.

Your research team collected data from all across the internet, but mostly just used information from Energy Information Administration and Bureau of Larbor Statistics .

For the time period 2012 to 2021, you found a correlation 0.9267308, r-squared of 0.8588300, 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]]
Shining a Light on Solar Power: Illuminating the Link between Solar Energy Production in Cabo Verde and the Number of Special Education Teachers in Illinois

[[ABSTRACT]]
The impact of solar power generation on the educational workforce is a topic that has been left in the shadows until now. This study aims to shed light on the connection between the solar power output of Cabo Verde and the employment of special education teachers in the state of Illinois. Utilizing data from the Energy Information Administration and the Bureau of Labor Statistics, we uncovered an unexpected correlation that is positively glaring. Our analysis revealed a remarkably sunny correlation coefficient of 0.9267308, with a p-value indicating statistical significance at p < 0.01 for the period spanning from 2012 to 2021. While it may seem like a shot in the dark, the findings of this study present an illuminating insight into the potential influence of solar power generation on educational employment dynamics. The rays of this correlation may be too bright to ignore, and serve as a beam of encouragement for further investigation into the enlightening intersection of renewable energy and educational staffing.

-------

And here is the methodology section that led to this result:
[[METHODOLOGY]]
INTRODUCTION
As the world grapples with the pressing need to transition towards sustainable energy sources, the spotlight has fallen on the potential impact of renewable energy on various aspects of society. Amid this fervent interest, the influence of solar power generation on the labor market, particularly in the education sector, has emerged as an area of burgeoning curiosity. In this context, the unearthing of a compelling association between solar energy production in Cabo Verde and the number of special education teachers in Illinois holds the promise of shedding light on previously unnoticed dynamics.
The deployment of solar energy has been heralded as a beacon of hope for a cleaner, greener future. As the sun's rays power photovoltaic cells and generate electricity in locations such as Cabo Verde, the implications for distant educational landscapes might not typically be the first thing to come to mind. However, as our research endeavors to reveal, there may be an unexpected and striking connection that warrants deliberate exploration.
By examining the solar power output of Cabo Verde and the employment of special education teachers in Illinois, we embark on an intellectual journey to uncover the nuanced interplay between these seemingly disparate domains. While one might anticipate a tenuous link at best, our investigation has uncovered a correlation that dazzles with its brightness – akin to a sudden ray of insight piercing through the clouds.
This study employs meticulous data analysis, drawing upon resources from the Energy Information Administration and the Bureau of Labor Statistics to unravel the threads of association. The correlation coefficient that emerged, a remarkably sunny 0.9267308, is undeniably eye-catching – a statistical illumination that beckons further examination and consideration.
In the pages that follow, we delve into the intricacies of this unexpected relationship, recognizing the potential significance of our findings as more than a mere flash in the pan. While it may seem like a leap to connect solar energy output to educational employment dynamics, the evidence we present advocates for a deeper acknowledgment of the radiance that renewable energy sources cast on the tapestry of education.
So, as we reflect upon the intersections of solar power and educational staffing, let us embrace the enlightenment that this study offers, and embark on a journey of scholarly exploration that seeks to bring these overlooked dynamics into the spotlight.

-------

Your response should start like this:

[[RESULTS]]



ChatGPT:

[[RESULTS]]

The analysis of the data uncovered a remarkable and positively glaring correlation between solar energy production in Cabo Verde and the number of special education teachers in Illinois. Over the period from 2012 to 2021, the correlation coefficient was found to be 0.9267308, indicating a strong, almost blinding association between these seemingly unrelated variables. This correlation was further supported by an r-squared value of 0.8588300, highlighting the robustness of the relationship. Importantly, the statistical significance of this correlation was underscored by a p-value of less than 0.01, implying a high level of confidence in the observed connection.

The striking relationship between solar power generation and the count of special education teachers in Illinois is visually illustrated in Fig. 1, which portrays a scatterplot showcasing the unmistakable alignment of these two variables. The data points are clustered around a linear pattern, radiating a sense of coherence that is in line with the numerical findings.

These findings, while certainly surprising, convey an intriguing insight into the potential influence of solar energy on educational staffing dynamics in a region distantly removed from its geographical origins. The strength of this correlation could be likened to the intensity of the sun's rays beaming down on an enlightening discovery, illuminating a path for further inquiry and exploration. The rays of this correlation may be too bright to ignore, and beckon future research to bask in the radiance of this unanticipated connection.


Model: gpt-3.5-turbo-1106
System prompt: You are a silly researcher writing an academic paper.You draft the discussion section of an academic research paper.
User prompt:
Pretend you are an academic researcher. Your writing sounds like it belongs in a medical or economics journal. However, you frequently crack jokes, make puns, or add unexpected twists in your writing.

Please make up the discussion section of an academic research paper presenting the findings of the connection between Solar power generated in Cabo Verde and The number of special education teachers in Illinois. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns. Stay serious and dry most of the time, but then occasionally let your wit shine through.

Limit your response to 500 tokens.

Here are the title, abstract, literature review, and results sections. Please harken back to 1-2 of the goofy items in the literature review, but pretend to take them completely seriously. Discuss how your results supported the prior research.

Do not write a conclusion. I will add a conclusion after this.

[[TITLE]]
Shining a Light on Solar Power: Illuminating the Link between Solar Energy Production in Cabo Verde and the Number of Special Education Teachers in Illinois

[[ABSTRACT]]
The impact of solar power generation on the educational workforce is a topic that has been left in the shadows until now. This study aims to shed light on the connection between the solar power output of Cabo Verde and the employment of special education teachers in the state of Illinois. Utilizing data from the Energy Information Administration and the Bureau of Labor Statistics, we uncovered an unexpected correlation that is positively glaring. Our analysis revealed a remarkably sunny correlation coefficient of 0.9267308, with a p-value indicating statistical significance at p < 0.01 for the period spanning from 2012 to 2021. While it may seem like a shot in the dark, the findings of this study present an illuminating insight into the potential influence of solar power generation on educational employment dynamics. The rays of this correlation may be too bright to ignore, and serve as a beam of encouragement for further investigation into the enlightening intersection of renewable energy and educational staffing.

[[LITERATURE REVIEW]]
In Smith's seminal work "Solar Power and its Implications on Labor Dynamics," the authors find a thorough examination of the effects of solar energy production on employment patterns across various sectors. While the focus of the study remains on traditional labor markets, the insights provided lay the groundwork for a broader consideration of solar energy's influence on specialized employment categories. Similarly, Doe's comprehensive analysis in "Renewable Energy and Workforce Dynamics" presents a meticulous exploration of the ways in which renewable energy sources, including solar power, impact the labor force.
Expanding beyond the realm of academic research, real-world accounts in "Solar Power in the Modern World" and "Educational Staffing Trends in the Midwest" provide additional context for the intersection of solar power generation and educational employment dynamics. Furthermore, the fiction novel "Sunny Skies and Special Education: A Tale of Unexpected Connections" offers a whimsical portrayal of the potential link between celestial energy and educational staffing, albeit in a purely imaginative context.
As the investigation delves deeper into the unexpected relationship between solar power output in Cabo Verde and the employment of special education teachers in Illinois, the authors must disclose a less conventional approach to literature review. Alongside scholarly texts and industry reports, sources such as the back labels of household cleaning products and fortune cookies were consulted to ensure a thorough exploration of all available material – an endeavor not without its surprise revelations.
The light-hearted pursuits of this unconventional literature review uncovered intriguing conversations regarding the sunny disposition interwoven with the employment of special education teachers. While the path taken may have been unconventional, the findings gathered shed a quirky perspective on the role of solar energy in educational staffing dynamics, echoing the lively and unexpected nature of the correlation under scrutiny.

[[RESULTS]]
The analysis of the data uncovered a remarkable and positively glaring correlation between solar energy production in Cabo Verde and the number of special education teachers in Illinois. Over the period from 2012 to 2021, the correlation coefficient was found to be 0.9267308, indicating a strong, almost blinding association between these seemingly unrelated variables. This correlation was further supported by an r-squared value of 0.8588300, highlighting the robustness of the relationship. Importantly, the statistical significance of this correlation was underscored by a p-value of less than 0.01, implying a high level of confidence in the observed connection.
The striking relationship between solar power generation and the count of special education teachers in Illinois is visually illustrated in Fig. 1, which portrays a scatterplot showcasing the unmistakable alignment of these two variables. The data points are clustered around a linear pattern, radiating a sense of coherence that is in line with the numerical findings.
These findings, while certainly surprising, convey an intriguing insight into the potential influence of solar energy on educational staffing dynamics in a region distantly removed from its geographical origins. The strength of this correlation could be likened to the intensity of the sun's rays beaming down on an enlightening discovery, illuminating a path for further inquiry and exploration. The rays of this correlation may be too bright to ignore, and beckon future research to bask in the radiance of this unanticipated connection.

-------

Your response should start like this:

[[DISCUSSION]]



ChatGPT:

In discussing the unexpectedly glaring correlation between solar power generation in Cabo Verde and the number of special education teachers in Illinois, it is imperative to acknowledge the initial incredulity that may accompany such a revelation. Much like an unexpected light bulb moment, our findings magnificently support the prior research presented in Smith's comprehensive exploration of solar power's labor market implications. The positively glaring relationship uncovered aligns seamlessly with the broader considerations of renewable energy's influence on specialized employment patterns. Similarly, Doe's meticulous analysis of renewable energy's impact on the labor force resonates with the brilliantly illuminated correlation we stumbled upon. The connection is as clear as day, lighting the way for further exploration into the intersection of celestial energy and educational staffing dynamics.

Despite the whimsical nature of the literature review, the earnest support for these unexpected sources cannot be overlooked. The unorthodox reliance on unconventional material, such as fortune cookies and household cleaning product labels, has undoubtedly illuminated a quirky new perspective on the role of solar energy in educational staffing dynamics, thereby underscoring the unpredictably sunny disposition interwoven with the employment of special education teachers.

The statistical robustness of the correlation, denoted by the remarkably high correlation coefficient and r-squared value, as well as the significantly low p-value, further underscores the credibility of our findings. The strength of this relationship is akin to a beacon of scholarly light, shining on an unanticipated discovery and beckoning future research to revel in the radiance of this newfound connection. In sum, our results are as bright as a sunny day in Cabo Verde, shedding light on the luminary influence of solar power on educational staffing dynamics in Illinois, and leaving us all basking in the warmth of this unexpected correlation.


Model: gpt-3.5-turbo-1106
System prompt: You are a silly researcher writing an academic paper.You draft the discussion section of goofy academic research papers that sounds like it belongs based on the title and abstract provided.
User prompt:
Pretend you are an academic researcher. Your writing sounds like it belongs in a medical or economics journal. However, you frequently crack jokes, make puns, or add unexpected twists in your writing.

Please make up a short conclusion of an academic research paper presenting the findings of the connection between Solar power generated in Cabo Verde and The number of special education teachers in Illinois. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns. Stay serious and dry most of the time, but then occasionally let your wit shine through.

Limit your response to 300 tokens. At the very end, assert that no more research is needed in this area.

Here are the title, abstract, introduction, and results sections.
[[TITLE]]
Shining a Light on Solar Power: Illuminating the Link between Solar Energy Production in Cabo Verde and the Number of Special Education Teachers in Illinois

[[ABSTRACT]]
The impact of solar power generation on the educational workforce is a topic that has been left in the shadows until now. This study aims to shed light on the connection between the solar power output of Cabo Verde and the employment of special education teachers in the state of Illinois. Utilizing data from the Energy Information Administration and the Bureau of Labor Statistics, we uncovered an unexpected correlation that is positively glaring. Our analysis revealed a remarkably sunny correlation coefficient of 0.9267308, with a p-value indicating statistical significance at p < 0.01 for the period spanning from 2012 to 2021. While it may seem like a shot in the dark, the findings of this study present an illuminating insight into the potential influence of solar power generation on educational employment dynamics. The rays of this correlation may be too bright to ignore, and serve as a beam of encouragement for further investigation into the enlightening intersection of renewable energy and educational staffing.

[[INTRDUCTION]]
INTRODUCTION
As the world grapples with the pressing need to transition towards sustainable energy sources, the spotlight has fallen on the potential impact of renewable energy on various aspects of society. Amid this fervent interest, the influence of solar power generation on the labor market, particularly in the education sector, has emerged as an area of burgeoning curiosity. In this context, the unearthing of a compelling association between solar energy production in Cabo Verde and the number of special education teachers in Illinois holds the promise of shedding light on previously unnoticed dynamics.
The deployment of solar energy has been heralded as a beacon of hope for a cleaner, greener future. As the sun's rays power photovoltaic cells and generate electricity in locations such as Cabo Verde, the implications for distant educational landscapes might not typically be the first thing to come to mind. However, as our research endeavors to reveal, there may be an unexpected and striking connection that warrants deliberate exploration.
By examining the solar power output of Cabo Verde and the employment of special education teachers in Illinois, we embark on an intellectual journey to uncover the nuanced interplay between these seemingly disparate domains. While one might anticipate a tenuous link at best, our investigation has uncovered a correlation that dazzles with its brightness – akin to a sudden ray of insight piercing through the clouds.
This study employs meticulous data analysis, drawing upon resources from the Energy Information Administration and the Bureau of Labor Statistics to unravel the threads of association. The correlation coefficient that emerged, a remarkably sunny 0.9267308, is undeniably eye-catching – a statistical illumination that beckons further examination and consideration.
In the pages that follow, we delve into the intricacies of this unexpected relationship, recognizing the potential significance of our findings as more than a mere flash in the pan. While it may seem like a leap to connect solar energy output to educational employment dynamics, the evidence we present advocates for a deeper acknowledgment of the radiance that renewable energy sources cast on the tapestry of education.
So, as we reflect upon the intersections of solar power and educational staffing, let us embrace the enlightenment that this study offers, and embark on a journey of scholarly exploration that seeks to bring these overlooked dynamics into the spotlight.

[[RESULTS]]
The analysis of the data uncovered a remarkable and positively glaring correlation between solar energy production in Cabo Verde and the number of special education teachers in Illinois. Over the period from 2012 to 2021, the correlation coefficient was found to be 0.9267308, indicating a strong, almost blinding association between these seemingly unrelated variables. This correlation was further supported by an r-squared value of 0.8588300, highlighting the robustness of the relationship. Importantly, the statistical significance of this correlation was underscored by a p-value of less than 0.01, implying a high level of confidence in the observed connection.
The striking relationship between solar power generation and the count of special education teachers in Illinois is visually illustrated in Fig. 1, which portrays a scatterplot showcasing the unmistakable alignment of these two variables. The data points are clustered around a linear pattern, radiating a sense of coherence that is in line with the numerical findings.
These findings, while certainly surprising, convey an intriguing insight into the potential influence of solar energy on educational staffing dynamics in a region distantly removed from its geographical origins. The strength of this correlation could be likened to the intensity of the sun's rays beaming down on an enlightening discovery, illuminating a path for further inquiry and exploration. The rays of this correlation may be too bright to ignore, and beckon future research to bask in the radiance of this unanticipated connection.

-------

Your response should start like this:

[[CONCLUSION]]



ChatGPT:

CONCLUSION

In conclusion, the results of this study have illuminated a remarkably sunny correlation between solar energy production in Cabo Verde and the number of special education teachers in Illinois. The statistical analysis revealed a positively glaring correlation coefficient of 0.9267308, akin to the strength of the sun's rays beaming down on an enlightening discovery. While one might expect this connection to be a mere flash in the pan, the robustness of the relationship, as evidenced by the r-squared value of 0.8588300, emphasizes the need to take this finding seriously. The scatterplot vividly portrays the unmistakable alignment of these variables, radiating a sense of coherence that is in line with the numerical findings.

While it may seem like a leap to connect solar energy output to educational employment dynamics, the evidence we present advocates for a deeper acknowledgment of the radiance that renewable energy sources cast on the tapestry of education. However, it is important to note that this study is not without limitations. Perhaps we are simply seeing things through solar-tinted glasses, and further inquiry may reveal alternative interpretations.

Based on the findings of this study, we assert that no more research is needed in this area. It is time to remove the shade from our eyes and recognize the brilliance of this unexpected correlation. The sun has indeed shone a light on the intersection of solar power and educational staffing, and it is an area that promises to bask in the radiance of further scholarly exploration.


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 Shining a Light on Solar Power: Illuminating the Link between Solar Energy Production in Cabo Verde and the Number of Special Education Teachers in Illinois. Respond only with the name of a fictional academic journal.

ChatGPT:

The Journal of Illuminating Interdisciplinary Research


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 Shining a Light on Solar Power: Illuminating the Link between Solar Energy Production in Cabo Verde and the Number of Special Education Teachers in Illinois. Respond only with the name of a fictional organization.

ChatGPT:

Sunshine Research Institute


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 Shining a Light on Solar Power: Illuminating the Link between Solar Energy Production in Cabo Verde and the Number of Special Education Teachers in Illinois with an abstract of The impact of solar power generation on the educational workforce is a topic that has been left in the shadows until now. This study aims to shed light on the connection between the solar power output of Cabo Verde and the employment of special education teachers in the state of Illinois. Utilizing data from the Energy Information Administration and the Bureau of Labor Statistics, we uncovered an unexpected correlation that is positively glaring. Our analysis revealed a remarkably sunny correlation coefficient of 0.9267308, with a p-value indicating statistical significance at p < 0.01 for the period spanning from 2012 to 2021. While it may seem like a shot in the dark, the findings of this study present an illuminating insight into the potential influence of solar power generation on educational employment dynamics. The rays of this correlation may be too bright to ignore, and serve as a beam of encouragement for further investigation into the enlightening intersection of renewable energy and educational staffing.

ChatGPT:

solar power, solar energy production, Cabo Verde, special education teachers, Illinois, correlation, renewable energy, educational employment dynamics

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



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Data details

Solar power generated in Cabo Verde
Detailed data title: Total solar power generated in Cabo Verde in billion kWh
Source: Energy Information Administration
See what else correlates with Solar power generated in Cabo Verde

The number of special education teachers in Illinois
Detailed data title: BLS estimate of special education teachers, kindergarten and elementary school in Illinois
Source: Bureau of Larbor Statistics
See what else correlates with The number of special education teachers in Illinois

Correlation r = 0.9267308 (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 = 0.8588300 (Coefficient of determination)
This means 85.9% of the change in the one variable (i.e., The number of special education teachers in Illinois) is predictable based on the change in the other (i.e., Solar power generated in Cabo Verde) over the 10 years from 2012 through 2021.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 0.00012. 0.0001153342213128171700000000
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 0.93 in 0.012% of random cases. Said differently, if you correlated 8,670 random variables Which I absolutely did.
with the same 9 degrees of freedom, Degrees of freedom is a measure of how many free components we are testing. In this case it is 9 because we have two variables measured over a period of 10 years. It's just the number of years minus ( the number of variables minus one ), which in this case simplifies to the number of years minus one.
you would randomly expect to find a correlation as strong as this one.

[ 0.71, 0.98 ] 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.
2012201320142015201620172018201920202021
Solar power generated in Cabo Verde (Billion kWh)0.0080.0070.0070.0060.0060.0070.0090.0090.0090.009
The number of special education teachers in Illinois (Laborers)10690849080406630637085109670110901174010350




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([0.008,0.007,0.007,0.006,0.006,0.007,0.009,0.009,0.009,0.009,])
array_2 = np.array([10690,8490,8040,6630,6370,8510,9670,11090,11740,10350,])
array_1_name = "Solar power generated in Cabo Verde"
array_2_name = "The number of special education teachers in Illinois"

# 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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You may re-use the images on this page for any purpose, even commercial purposes, without asking for permission. The only requirement is that you attribute Tyler Vigen. Attribution can take many different forms. If you leave the "tylervigen.com" link in the image, that satisfies it just fine. If you remove it and move it to a footnote, that's fine too. You can also just write "Charts courtesy of Tyler Vigen" at the bottom of an article.

You do not need to attribute "the spurious correlations website," and you don't even need to link here if you don't want to. I don't gain anything from pageviews. There are no ads on this site, there is nothing for sale, and I am not for hire.

For the record, I am just one person. Tyler Vigen, he/him/his. I do have degrees, but they should not go after my name unless you want to annoy my wife. If that is your goal, then go ahead and cite me as "Tyler Vigen, A.A. A.A.S. B.A. J.D." Otherwise it is just "Tyler Vigen."

When spoken, my last name is pronounced "vegan," like I don't eat meat.

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Correlation ID: 2110 · Black Variable ID: 23568 · Red Variable ID: 19041
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