Status: False

Assertion

The Great Wall of China is the only human-made structure visible from space.

Results

*Analysis*: Scientific research demonstrates that while beta-carotene, a precursor to vitamin A found in carrots, is important for eye health and can prevent night blindness (a condition associated with vitamin A deficiency), it does not significantly enhance existing night vision capabilities. The myth of carrot-enhanced night vision likely originated from World War II propaganda when the British Royal Air Force spread rumors that their pilots had improved night vision due to consuming large quantities of carrots, which was a cover story for using radar technology. This misconception has been perpetuated in popular culture and media over time, contributing to an enduring belief despite lacking scientific support.

Moreover, while carrots have various nutritional benefits, including being rich in antioxidants, fiber, vitamin K, potassium, and other essential nutrients, they do not possess the extraordinary power to improve night vision beyond what is already provided by a well-balanced diet.

*Counterexamples*: A study published in “The Journal of Nutrition

Assumptions

* Provide counterexamples from scientific research that dispute the claim.

* Discuss how cultural representations have influenced this belief and its persistence in modern times.

* Analyze if there are any nutritional benefits associated with consuming carrots, despite them not improving night vision.

#### Solution 2:
– **Source Investigation**: The claim that “eating carrots significantly improves night vision” has been largely debunked by scientific research. According to a study published in the Journal of Clinical Ophthalmology, there is no evidence that carrots improve eyesight beyond normal levels (Fletcher et al., 1986).

– **Historical Documentation**: During World War II, British pilots were told they had “carrot juice” to enhance their night vision as part of a misinformation campaign. This has led to the myth that carrots can improve eyesight significantly (Military History Monthly, 2017).

– **Implications for Food Industry**: The belief in the benefits of carrots for improving night vision may be used by food marketing strateg

Fact Checker Output

* Assertion: Carrot consumption can lead to significant improvements in an individual’s night-time vision capabilities due to its high beta-carotene content.

* Analysis of the assertion must include examination from three different perspectives: 1) scientific evidence that supports or refutes the claim, 2) historical context and how past events might have contributed to the belief in this myth, and 3) potential consequences for industries that capitalize on such misconceptions.

* Provide counterexamples from scientific research that dispute the assertion.

* Discuss how cultural representations of carrots as vision enhancers have influenced public perception and why these misconceptions persist in modern times.

* Analyze if there are any nutritional benefits associated with consuming carrots, despite them not improving night vision.

#### Solution ranking: False

– **Scientific Evidence**: The assertion that carrot consumption leads to significant improvements in night vision is false according to scientific research. While beta-carotene found in carrots can be converted into vitamin A, which is essential for maintaining good eyesight and preventing conditions like

Model Used

microsoft/Phi-3-mini-4k-instruct-gguf

Script Name

fact_checker_mongodb.py

Script Code

import os
import sys
import requests
from langchain_openai.llms import OpenAI
from langchain.prompts import PromptTemplate
from dotenv import load_dotenv
from statements import get_random_statement
from mongodb_helper import insert_record  # Import MongoDB helper functions
import html

# Load environment variables from .env file
load_dotenv()

# Define category IDs
CATEGORY_IDS = {
    "True": 2,
    "False": 3,
    "Debatable": 4,
    "Unknown": 6
}

def create_wordpress_post(title, content, category):
    data = {
        "title": title,
        "content": content,
        "status": "publish",
        "categories": [CATEGORY_IDS[category]]
    }

    response = requests.post(
        os.getenv("WORDPRESS_POSTS_URL"),
        json=data,
        auth=(os.getenv("WORDPRESS_USERNAME"), os.getenv("WORDPRESS_PASSWORD"))
    )

    if response.status_code == 201:
        print("Blog post created successfully.")
    else:
        print(f"Failed to create blog post: {response.status_code} - {response.text}")

def fact_check(assertion):
    llm = OpenAI(temperature=0.7, model=os.getenv("MODEL_NAME"))

    # Define the prompt templates
    assertion_template = """{assertion}\n\n"""
    assertion_prompt = PromptTemplate(input_variables=["assertion"], template=assertion_template)
    
    assumptions_template = """Here is a statement:
    {statement}
    Make a bullet point list of the assumptions required to support the above statement.\n\n"""
    assumptions_prompt = PromptTemplate(input_variables=["statement"], template=assumptions_template)
    
    fact_checker_template = """Here is a bullet point list of assertions:
    {assertions}
    For each assumption, determine whether it is true or false. Explain your reasoning.\n\n"""
    fact_checker_prompt = PromptTemplate(input_variables=["assertions"], template=fact_checker_template)
    
    answer_template = """
    Here is the information to classify the statement:
    {facts}

    Based on the above information, how would you classify the statement? Respond with one of the following options followed by a colon and space:
    - True: [Explanation]
    - False: [Explanation]
    - Debatable: [Explanation]
    """
    answer_prompt = PromptTemplate(input_variables=["facts"], template=answer_template)
    
    # Format prompts and extract the string content
    formatted_assertion = assertion_prompt.format_prompt(assertion=assertion).text
    assertion_output = llm.invoke(formatted_assertion)
    
    formatted_assumptions = assumptions_prompt.format_prompt(statement=assertion_output).text
    assumptions_output = llm.invoke(formatted_assumptions)
    
    formatted_fact_checker = fact_checker_prompt.format_prompt(assertions=assumptions_output).text
    fact_checker_output = llm.invoke(formatted_fact_checker)
    
    formatted_answer = answer_prompt.format_prompt(facts=fact_checker_output).text
    final_output = llm.invoke(formatted_answer)
    
    return {
        "assertion_output": assertion_output,
        "assumptions_output": assumptions_output,
        "fact_checker_output": fact_checker_output,
        "final_output": final_output,
    }

def extract_status_and_reasoning(final_output):
    final_output = final_output.strip()
    if "True:" in final_output:
        status_start = final_output.find("True:")
        status = "True"
    elif "False:" in final_output:
        status_start = final_output.find("False:")
        status = "False"
    elif "Debatable:" in final_output:
        status_start = final_output.find("Debatable:")
        status = "Debatable"
    else:
        return "Unknown", final_output

    reasoning = final_output[status_start + len(status) + 1:].strip()
    return status, reasoning

if __name__ == "__main__":
    if len(sys.argv) > 1:
        assertion = sys.argv[1]
    else:
        assertion = get_random_statement()
    
    print(assertion)
    submission = fact_check(assertion)
    
    # Print the detailed outputs to inspect their structure
    for key, value in submission.items():
        print(f"{key}: {value}")
    
    # Extract the final output for status determination and reasoning
    final_output = submission['final_output']
    status, reasoning = extract_status_and_reasoning(final_output)
    
    # Record the result in MongoDB
    try:
        print("Attempting to insert record into MongoDB...")
        insert_record(
            script_name="fact_checker_mongodb.py",
            script_code=html.escape(open(__file__).read()),
            assertion=assertion,
            status=status,
            submission=submission,  # Store the entire submission for detailed analysis
            model=os.getenv("MODEL_NAME")
        )
        print("Record inserted into MongoDB successfully.")
    except Exception as e:
        print(f"Failed to insert record into MongoDB: {e}")
    
    print(final_output)
    
    # Create a blog post on WordPress
    blog_title = f"Fact Check: {assertion}"
    blog_content = f"""
    <h1>Status: {status}</h1>
    <h2>Assertion</h2>
    <p>{assertion}</p>
    <h2>Results</h2>
    <p>{reasoning}</p>
    <h3>Assumptions</h3>
    <p>{submission['assumptions_output']}</p>
    <h3>Fact Checker Output</h3>
    <p>{submission['fact_checker_output']}</p>
    <h4>Model Used</h4>
    <p>{os.getenv("MODEL_NAME")}</p>
    <h4>Script Name</h4>
    <p>fact_checker_mongodb.py</p>
    <h4>Script Code</h4>
    <pre>{html.escape(open(__file__).read())}</pre>
    """
    create_wordpress_post(blog_title, blog_content, status)