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AI explanation

^{Show image generation prompt} Model: dalle-3
Prompt: The prompt's suggested style is a comic-book-style illustration from the 1960s with muted colors and a dotted halftone pattern. Here's an updated prompt to enhance the visuals: Title: "The Cycle of Renewal" Description: In a vast cornfield under the midday sun, rows of genetically modified corn extend to the horizon. The vibrant green of the corn contrasts against the golden soil and deep blue sky. A conveyor belt snakes through the field, transporting the harvested corn to a nearby processing facility. The conveyor belt is depicted with mechanical details and retro designs, reminiscent of the 1960s. At the processing facility, workers clad in vintage overalls and caps oversee the transformation of corn into various consumer products. The scene is filled with machinery and activity, with the muted colors evoking a nostalgic atmosphere. The dotted halftone pattern adds an abstract touch to the industrial setting, giving it a vintage comic book feel. In the background, workers gather the discarded cornhusks and load them onto trucks. The trucks, portrayed with a classic design, depart for Nicaragua, their journey hinted at through a stylized map in the distance. In Nicaragua, the trucks arrive amidst picturesque landscapes. The cornhusks are unloaded and used as a renewable energy source for hydroelectric plants. The rushing water from the plants is represented with dynamic lines and patterns, emphasizing the generation of hydropower energy. Resonating with the vintage style, the visual depiction captures the sense of renewal and progress as the electricity powers nearby communities. The overall scene is rich in detail, capturing the interconnectedness of industrial processes and environmental sustainability, all within the distinctive aesthetic of a 1960s comic-book-style illustration. No words or speech bubbles are included, allowing the visuals to tell the story. The depiction should include only scenes, people, and objects, it should not include text or speech bubbles.

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This image generation prompt was also generated by AI, using the following sequence:

Model: gpt-3.5-turbo-1106
System prompt: You select one of a few potential scene descriptors that seems to go along with the variables described. You respond only with the scene descriptions provided and say nothing else.
User prompt: Here are two variables: GMO use in corn and Hydopower energy generated in Nicaragua. 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: Parallax art Celtic art Sci-fi fantacy a Polaroid photo A comic-book-style illustration with muted colors and a dotted halftone pattern from the 1960s
ChatGPT: A comic-book-style illustration with muted colors and a dotted halftone pattern from the 1960s

Model: gpt-3.5-turbo-1106
System prompt: You describe simple visual scenes that depict the two variables stated. You combine both variables into one scene.
User prompt: Please describe a visual scene that prominently depicts both GMO use in corn and Hydopower energy generated in Nicaragua, in the following situation: As GMO corn production increased, it also led to an increase in cornhusk waste, which was then used as a renewable energy source to power hydroelectric plants in Nicaragua, effectively boosting hydropower energy generation.. Keep the description simple.
ChatGPT: In a vast cornfield, rows of genetically modified corn stretch out as far as the eye can see. A conveyor belt transports the harvested corn to a processing facility, where the corn is turned into consumer products. Meanwhile, workers gather the cornhusks and load them into trucks. These trucks travel to Nicaragua, where the cornhusks are used as a renewable energy source to power hydroelectric plants. The rushing water from the plants generates a significant amount of hydropower energy, providing electricity to nearby communities.

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: [ A comic-book-style illustration with muted colors and a dotted halftone pattern from the 1960s ] and depicting this scene: In a vast cornfield, rows of genetically modified corn stretch out as far as the eye can see. A conveyor belt transports the harvested corn to a processing facility, where the corn is turned into consumer products. Meanwhile, workers gather the cornhusks and load them into trucks. These trucks travel to Nicaragua, where the cornhusks are used as a renewable energy source to power hydroelectric plants. The rushing water from the plants generates a significant amount of hydropower energy, providing electricity to nearby communities. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: The prompt's suggested style is a comic-book-style illustration from the 1960s with muted colors and a dotted halftone pattern. Here's an updated prompt to enhance the visuals: Title: "The Cycle of Renewal" Description: In a vast cornfield under the midday sun, rows of genetically modified corn extend to the horizon. The vibrant green of the corn contrasts against the golden soil and deep blue sky. A conveyor belt snakes through the field, transporting the harvested corn to a nearby processing facility. The conveyor belt is depicted with mechanical details and retro designs, reminiscent of the 1960s. At the processing facility, workers clad in vintage overalls and caps oversee the transformation of corn into various consumer products. The scene is filled with machinery and activity, with the muted colors evoking a nostalgic atmosphere. The dotted halftone pattern adds an abstract touch to the industrial setting, giving it a vintage comic book feel. In the background, workers gather the discarded cornhusks and load them onto trucks. The trucks, portrayed with a classic design, depart for Nicaragua, their journey hinted at through a stylized map in the distance. In Nicaragua, the trucks arrive amidst picturesque landscapes. The cornhusks are unloaded and used as a renewable energy source for hydroelectric plants. The rushing water from the plants is represented with dynamic lines and patterns, emphasizing the generation of hydropower energy. Resonating with the vintage style, the visual depiction captures the sense of renewal and progress as the electricity powers nearby communities. The overall scene is rich in detail, capturing the interconnectedness of industrial processes and environmental sustainability, all within the distinctive aesthetic of a 1960s comic-book-style illustration. No words or speech bubbles are included, allowing the visuals to tell the story.

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*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.

AI academic paper

Kernels of Truth: Corny Connections Between GMO Use in Corn and Hydro-power in Nicaragua Journal of Agricultural Innovations Jan 2024 Reminder: This paper is AI-generated. Not real!

Problem variable:	Which variable has a problem? GMO use in corn Hydopower energy generated in Nicaragua Neither / both
Issue:	What is the issue? The data is incorrect The chart isn't displaying properly There is a typo The source link is incorrect A calculation is wrong Something else
Additional details: Optional
Confirm you are a human:

Data details

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^Note Because these pages are automatically generated, it's possible that the two variables you are viewing are in fact causually related. I take steps to prevent the obvious ones from showing on the site (I don't let data about the weather in one city correlate with the weather in a neighboring city, for example), but sometimes they still pop up. If they are related, cool! You found a loophole.
   no direct connection between these variables, despite what the AI says above. This is exacerbated by the fact that I used "Years" as the base variable. Lots of things happen in a year that are not related to each other! Most studies would use something like "one person" in stead of "one year" to be the "thing" studied.
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^Note Personally I don't find any p-value calculation to be 'simple,' but you know what I mean.
   p-value calculation does not take this into account, so mathematically it appears less probable than it really is.

Try it yourself

# 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([25,26,34,40,47,52,61,73,80,85,86,88,88,90,93,92,92,92,92,92,92,93,])
array_2 = np.array([0.208,0.195,0.3,0.294,0.318,0.429,0.305,0.303,0.52947,0.294,0.499,0.44,0.415,0.451,0.391,0.292,0.422,0.463,0.410892,0.226986,0.574471,0.574471,])
array_1_name = "GMO use in corn"
array_2_name = "Hydopower energy generated in Nicaragua"

# Perform the calculation
print(f"Calculating the correlation between {array_1_name} and {array_2_name}...")
correlation, r_squared, p_value = calculate_correlation(array_1, array_2)

# Print the results
print("Correlation Coefficient:", correlation)
print("R-squared:", r_squared)
print("P-value:", p_value)

Reuseable content

• High resolution line chart ^Note The image linked here is a Scalable Vector Graphic (SVG). It is the highest resolution that is possible to achieve. It scales up beyond the size of the observable universe without pixelating. You do not need to email me asking if I have a higher resolution image. I do not. The physical limitations of our universe prevent me from providing you with an image that is any higher resolution than this one.
  
  If you insert it into a PowerPoint presentation (a tool well-known for managing things that are the scale of the universe), you can right-click > "Ungroup" or "Create Shape" and then edit the lines and text directly. You can also change the colors this way.
  
  Alternatively you can use a tool like Inkscape.
  
• High resolution line chart, optimized for mobile
• Alternative high resolution line chart
• Scatterplot
• Portable line chart (png)
• Portable line chart (png), optimized for mobile
• Line chart for only GMO use in corn
• Line chart for only Hydopower energy generated in Nicaragua
• AI-generated correlation image
• The spurious research paper: Kernels of Truth: Corny Connections Between GMO Use in Corn and Hydro-power in Nicaragua