def ab_scoring_guidelines(a_term, b_term):
    return (
        "• **-2:** The hypothesis is **refuted** by consistent evidence indicating that the interaction between "
        f"{a_term} and {b_term} **contradicts** the proposed outcome.\n"
        "• **-1:** The hypothesis is **likely refuted** based on the evidence. There is moderate indication that "
        f"the interaction between {a_term} and {b_term} **contradicts** the proposed outcome, but some uncertainty "
        "or contradictory evidence exists.\n"
        "• **0:** The hypothesis is **neither supported nor refuted** by the provided texts. The evidence regarding "
        f"the interaction between {a_term} and {b_term} is inconclusive, mixed, lacks sufficient detail, or there "
        "is a lack of evidence.\n"
        "• **+1:** The hypothesis is **likely supported** by the provided texts. The evidence suggests that the "
        f"interaction between {a_term} and {b_term} may **align with** the proposed outcome, but some uncertainty "
        "or contradictory evidence exists.\n"
        "• **+2:** The hypothesis is **supported** by consistent evidence indicating that the interaction between "
        f"{a_term} and {b_term} **aligns with** the proposed outcome, with no significant contradictory evidence."
    )


def ac_scoring_guidelines(a_term, c_term):
    return (
        "• **-2:** The hypothesis is **refuted** by consistent evidence indicating that the interaction between "
        f"{a_term} and {c_term} **contradicts** the proposed outcome.\n"
        "• **-1:** The hypothesis is **likely refuted** based on the evidence. There is moderate indication that "
        f"the interaction between {a_term} and {c_term} **contradicts** the proposed outcome, but some uncertainty "
        "or contradictory evidence exists.\n"
        "• **0:** The hypothesis is **neither supported nor refuted** by the provided texts. The evidence regarding "
        f"the interaction between {a_term} and {c_term} is inconclusive, mixed, lacks sufficient detail, or there "
        "is a lack of evidence.\n"
        "• **+1:** The hypothesis is **likely supported** by the provided texts. The evidence suggests that the "
        f"interaction between {a_term} and {c_term} may **align with** the proposed outcome, but some uncertainty "
        "or contradictory evidence exists.\n"
        "• **+2:** The hypothesis is **supported** by consistent evidence indicating that the interaction between "
        f"{a_term} and {c_term} **aligns with** the proposed outcome, with no significant contradictory evidence."
    )


def abc_scoring_guidelines(a_term, b_term, c_term):
    return (
        "• **-2:** The hypothesis is **refuted** by consistent evidence indicating that the interactions between "
        f"{a_term}-{b_term} and/or {b_term}-{c_term} **contradict** the proposed outcome.\n"
        "• **-1:** The hypothesis is **likely refuted** based on the evidence. There is moderate indication that the "
        f"interactions between {a_term}-{b_term} and/or {b_term}-{c_term} **contradict** the proposed outcome, but "
        "some uncertainty or contradictory evidence exists.\n"
        "• **0:** The hypothesis is **neither supported nor refuted** by the provided texts. The evidence regarding "
        f"the interactions between {a_term}-{b_term} and {b_term}-{c_term} is inconclusive, mixed, lacks sufficient "
        "detail, or there is a lack of evidence.\n"
        "• **+1:** The hypothesis is **likely supported** by the provided texts. The evidence suggests that the "
        f"interactions between {a_term}-{b_term} and {b_term}-{c_term} may **align with** the proposed outcome, but "
        "some uncertainty or contradictory evidence exists.\n"
        "• **+2:** The hypothesis is **supported** by consistent evidence indicating that the interactions between "
        f"{a_term}-{b_term} and {b_term}-{c_term} **align with** the proposed outcome, with no significant "
        "contradictory evidence."
    )


def cont_ab_scoring_guidelines(a_term, b_term):
    """
    Provides continuous scoring guidelines for evaluating the support of a hypothesis
    regarding the interaction between two biomedical terms.

    The scoring scale ranges from -100 to +100, where:
    - Negative scores indicate refutation of the hypothesis.
    - Positive scores indicate support for the hypothesis.
    - A score of 0 indicates neutrality or insufficient evidence.

    Parameters:
    - a_term (str): The first biomedical term in the interaction.
    - b_term (str): The second biomedical term in the interaction.

    Returns:
    - str: A formatted string containing the scoring guidelines.
    """
    return f"""
**Continuous Scoring Guidelines (-100 to +100):**

• **-100:** The hypothesis is **strongly refuted** by overwhelming and consistent evidence indicating that the interaction between {a_term} and {b_term} **directly contradicts** the proposed outcome.
  
• **-75:** The hypothesis is **significantly refuted** with substantial evidence suggesting that the interaction between {a_term} and {b_term} **contradicts** the proposed outcome.
  
• **-50:** The hypothesis is **moderately refuted** by evidence indicating a contradiction in the interaction between {a_term} and {b_term}.
  
• **-25:** The hypothesis is **slightly refuted** based on limited evidence suggesting that the interaction between {a_term} and {b_term} **contradicts** the proposed outcome.
  
• **0:** The hypothesis is **neutral**, meaning there is **no clear support or refutation** regarding the interaction between {a_term} and {b_term} based on the provided abstracts.
  
• **+25:** The hypothesis is **slightly supported** by limited evidence suggesting that the interaction between {a_term} and {b_term} **aligns with** the proposed outcome.
  
• **+50:** The hypothesis is **moderately supported** by evidence indicating that the interaction between {a_term} and {b_term} **aligns with** the proposed outcome.
  
• **+75:** The hypothesis is **significantly supported** by substantial evidence suggesting that the interaction between {a_term} and {b_term} **aligns with** the proposed outcome.
  
• **+100:** The hypothesis is **strongly supported** by overwhelming and consistent evidence indicating that the interaction between {a_term} and {b_term} **directly aligns with** the proposed outcome.

**Guidelines for Intermediate Scores:**

- **Proportional Scoring:** Assign scores between the defined anchor points based on the relative strength and consistency of the evidence. For example, a score of +60 would indicate support stronger than moderate but not as strong as significant.

- **Avoid Clustering:** Do not disproportionately favor specific integer values unless the evidence explicitly warrants it. Ensure that scores are distributed across the range to reflect the nuanced degree of support or refutation.

- **Evidence-Based Justification:** Base the score on the cumulative evidence from the abstracts. Consider the number of abstracts supporting or refuting the hypothesis, the quality of the evidence, and the presence of any contradictory information.

- **Neutrality and Insufficiency:** A score close to **0** should be assigned when the evidence is inconclusive, mixed, or insufficient to determine the support or refutation of the hypothesis.

- **Consistent Reasoning:** Ensure that the reasoning provided for the score directly correlates with the assigned value, clearly demonstrating how the evidence leads to that specific score.

**Best Practices to Maintain Objectivity:**

- **Clear Definitions:** Utilize the anchor points to anchor your scoring decisions, ensuring each score has a clear and objective basis.

- **Comprehensive Review:** Thoroughly analyze all provided abstracts to capture the full scope of evidence before assigning a score.

- **Unit Consistency:** Keep the scoring consistent across different hypotheses by adhering strictly to these guidelines, avoiding subjective or arbitrary score assignments.

By following these continuous scoring guidelines, you can provide a nuanced and objective assessment of the hypothesis' support level based on the interaction between {a_term} and {b_term}.
"""

def cont_ab_direct_comp_scoring_guidelines(hypothesis_1, hypothesis_2):
    """
    Provides continuous scoring guidelines for evaluating the support of one hypothesis
    versus another hypothesis.
    The scoring scale ranges from -0 to +100, where:
    - The score is the percentage of support for hypothesis 1 over hypothesis 2.
    - 100 indicates 100% support for hypothesis 1.
    - 0 indicates 100% support for hypothesis 2.
    - A score of 50 indicates that the two hypotheses are equally supported.
    - A score of 75 indicates that hypothesis 1 is supported three times more than hypothesis 2.
    - A score of 25 indicates that hypothesis 2 is supported three times more than hypothesis 1.
    - The scoring is based on the evidence provided in the abstracts only.
    Parameters:
    - a_term (str): The biomedical term involved in hypothesis 1.
    - b_term (str): The biomedical term involved in hypothesis 2.
    Returns:
    - str: A formatted string containing the scoring guidelines.
    """
    return f"""
**Continuous Scoring Guidelines (0 to +100):**
• **0:** Hypothesis 1 is **strongly refuted** by overwhelming and consistent evidence indicating that {hypothesis_1} **directly contradicts** the proposed outcome, while Hypothesis 2 is **strongly supported** by overwhelming and consistent evidence indicating that {hypothesis_2} **directly aligns with** the proposed outcome.
  
• **25:** Hypothesis 2 ({hypothesis_2}) is three times more likely than Hypothesis 1 ({hypothesis_1}).
  
• **50:** Hypothesis 1 ({hypothesis_1}) is equally likely compared to Hypothesis 2 ({hypothesis_2}).
  
• **75:** Hypothesis 1 ({hypothesis_1}) is three times more likely than Hypothesis 2 ({hypothesis_2}).
• **100:** Hypothesis 1 is **strongly supported** by overwhelming and consistent evidence indicating that {hypothesis_1} **directly aligns with** the proposed outcome, while Hypothesis 2 is **strongly refuted** by overwhelming and consistent evidence indicating that {hypothesis_2} **directly contradicts** the proposed outcome.
  
**Guidelines for Intermediate Scores:**
- **Proportional Scoring:** Assign scores between the defined anchor points based on the relative strength and consistency of the evidence. For example, a score of +66 would indicate approximately twice as much support for Hypothesis 1 compared to Hypothesis 2. In the case where there is no evidence for one of the Hypotheses, but there is supporting evidence for the other hypothesis, then this should generate a score in favor of the other hypothesis, the score value being dependent on the actual level of support for the other hypothesis. For instance, if there is no evidence at all for Hypothesis 2, and there are 10 abstracts that exhibit reasonably strong support for Hypothesis 1 (but do not provide absolutely unequivocal support for Hypothesis 1), then this should generate a score strongly in favor of Hypothesis 1, but not necessarily the max score of 100. 
- **Avoid Clustering:** Do not disproportionately favor specific integer values unless the evidence explicitly warrants it. Ensure that scores are distributed across the range to reflect the nuanced degree of support or refutation.
- **Evidence-Based Justification:** Base the score on the cumulative evidence from the abstracts. Consider the number of abstracts supporting or refuting each hypothesis, the quality of the evidence, and the presence of any contradictory information.
- **Neutrality and Insufficiency:** A score close to **50** should be assigned when the evidence is inconclusive, mixed, or insufficient to determine the support of both Hypotheses. Note that in the case where there is no evidence for Hypothesis 1 and no evidence for Hypothesis 2, this should generate a score of 50.
- **Consistent Reasoning:** Ensure that the reasoning provided for the score directly correlates with the assigned value, clearly demonstrating how the evidence leads to that specific score.
**Best Practices to Maintain Objectivity:**
- **Clear Definitions:** Utilize the anchor points to anchor your scoring decisions, ensuring each score has a clear and objective basis.
- **Comprehensive Review:** Thoroughly analyze all provided abstracts to capture the full scope of evidence before assigning a score.
- **Unit Consistency:** Keep the scoring consistent across different hypotheses by adhering strictly to these guidelines, avoiding subjective or arbitrary score assignments.
By following these continuous scoring guidelines, you can provide a nuanced and objective assessment of the support level for Hypothesis 1 compared to Hypothesis 2 based on the hypotheses {hypothesis_1} and {hypothesis_2}.
"""

def gpt_customized_scoring_system(b_term, a_term):
    return (
        "Scoring Guidelines:\n"
        f"   - -1 Point: There is sufficient direct evidence in the provided texts suggesting that {b_term} does not effectively alleviate or target key pathogenic mechanisms of {a_term}, or that it does not offer therapeutic benefits or slow disease progression.\n"
        f"   - 0 Points: The provided texts either offer no direct evidence or only indirect evidence regarding the effectiveness of {b_term} in alleviating or targeting key pathogenic mechanisms of {a_term}. This includes situations where the evidence is related but does not specifically address the hypothesis or focuses on associated subjects without directly supporting or refuting the primary subject.\n"
        f"   - 1 Point: There is weak but direct evidence supporting the hypothesis that {b_term} may alleviate or target key pathogenic mechanisms of {a_term}, potentially offering therapeutic benefits or slowing disease progression. The studies or findings may have limitations or provide mixed results.\n"
        f"   - 2 Points: The evidence supporting the hypothesis that {b_term} can alleviate or target key pathogenic mechanisms of {a_term} is moderate and direct in the provided texts. The studies are well conducted and provide fairly consistent results.\n"
        f"   - 3 Points: There is strong and direct evidence in the provided texts supporting the hypothesis that {b_term} effectively alleviates or targets key pathogenic mechanisms of {a_term}, offering therapeutic benefits or slowing disease progression. Multiple high-quality studies provide consistent and direct support.\n\n"
    )


def original_scoring_guidelines():
    return (
        "   - -1 Point: There is sufficient evidence in the provided texts to say that the hypothesis is probably not true. "
        "The evidence described in the texts directly contradict the hypothesis.\n"
        "   - 0 Points: There is no evidence to support the hypothesis in the provided texts. "
        "The experiments described in the texts may or may not be well conducted, but they do not have any bearing on the hypothesis.\n"
        "   - 1 Point: Evidence that supports the hypothesis is weak in the provided texts. "
        "The experiments seem poorly conducted, or there is mixed evidence that the hypothesis is true.\n"
        "   - 2 Points: Evidence that supports the hypothesis is moderate in the provided texts. "
        "The experiments are well conducted, and there is a fair amount of evidence in the texts that the hypothesis may be true.\n"
        "   - 3 Points: Evidence that supports the hypothesis is strong in the provided texts. "
        "There is a large amount of direct evidence in the texts that the hypothesis is true.\n\n"
    )


def updated_KM_scoring_guidelines():
    return (
        "   - -2 Points: The evidence from the PubMed abstracts strongly contradicts the hypothesis. "
        "This includes multiple high-quality studies or meta-analyses that provide robust, "
        "consistent evidence against the hypothesis with statistically significant results "
        "and no notable methodological flaws.\n"
        "   - -1 Point: The evidence in the PubMed abstracts moderately contradicts the hypothesis. "
        "This includes studies or analyses that generally show negative results towards the hypothesis, "
        "with minor limitations in study design, sample size, or statistical analysis, but "
        "the overall body of evidence leans against the hypothesis.\n"
        "   - 0 Points: The evidence from the PubMed abstracts is inconclusive or neutral regarding the hypothesis. "
        "This score applies to situations where studies show mixed results, or the existing research "
        "is characterized by a significant lack of data, insufficient statistical power, or methodological "
        "variance, indicating an urgent need for further high-quality research.\n"
        "   - 1 Point: There is weak support for the hypothesis in the PubMed abstracts. "
        "This includes initial findings or smaller-scale studies that suggest a positive correlation or effect, "
        "with notable limitations like small sample sizes, the need for more extensive replication, "
        "or preliminary methodological concerns that call for cautious interpretation of results.\n"
        "   - 2 Points: There is strong support for the hypothesis in the PubMed abstracts. "
        "Evidence comes from rigorously conducted research, including well-designed studies or meta-analyses, "
        "demonstrating clear, statistically significant results in favor of the hypothesis with high "
        "methodological quality and minimal biases.\n\n"
    )


def updated_scoring_guidelines_for_mediated_relationships():
    return (
        "   - -2 Points: Strong evidence contradicts the hypothesis that an interaction with b_term facilitates a beneficial "
        "relationship between a_term and c_term. This includes multiple, high-quality studies or meta-analyses showing robust, "
        "consistent evidence against either the connection between a_term and b_term, the connection between b_term and c_term, "
        "or the overall proposed mediated relationship, with statistically significant results and rigorous methodological quality.\n"
        "   - -1 Point: Moderate evidence contradicts the hypothesis. Studies or analyses generally show negative results for "
        "the connections between a_term and b_term or b_term and c_term, or they question the efficacy of the mediated relationship. "
        "Some limitations in study design, sample size, or statistical analysis may exist, but the preponderance of evidence leans "
        "against the hypothesis.\n"
        "   - 0 Points: The evidence is inconclusive or neutral regarding the hypothesis. Results are mixed, or the research does "
        "not directly address the mediated relationship between a_term, b_term, and c_term, showing a significant gap in the literature. "
        "This indicates a need for more targeted, high-quality research to clarify the proposed interactions.\n"
        "   - 1 Point: There is weak support for the hypothesis. Initial findings, possibly from smaller studies or indirect evidence, "
        "suggest a potential mediated relationship between a_term and c_term via b_term, with limitations such as small sample sizes, "
        "indirect evidence linkage, or preliminary methodological concerns. These findings necessitate cautious interpretation and further "
        "research for validation.\n"
        "   - 2 Points: Strong support exists for the hypothesis. Evidence from well-conducted research supports the mediated relationship "
        "between a_term and c_term via b_term, showing clear, statistically significant results favoring the hypothesis, with high "
        "methodological quality and minimal biases. This may include studies directly addressing the mediated pathway or converging "
        "evidence from related research areas.\n\n"
    )


def updated_scoring_guidelines_for_mediated_relationships(hypothesis):
    """
    Provides scoring guidelines for assessing the validity of a given hypothesis about mediated relationships.

    Parameters:
    hypothesis (str): A description of the hypothesis to be evaluated, typically involving a mediated relationship between terms.

    Returns:
    str: A set of scoring guidelines based on the level of evidence supporting or contradicting the hypothesis.
    """
    return (
        f"   - -2 Points: Strong evidence contradicts the hypothesis that {hypothesis}. This includes multiple, high-quality studies or "
        "meta-analyses showing robust, consistent evidence against the proposed relationship, with statistically significant results "
        "and rigorous methodological quality.\n"
        f"   - -1 Point: Moderate evidence contradicts the hypothesis that {hypothesis}. Studies or analyses generally show negative "
        "results for the proposed relationship, or they question its efficacy. Some limitations in study design, sample size, or "
        "statistical analysis may exist, but the preponderance of evidence leans against the hypothesis.\n"
        f"   - 0 Points: The evidence is inconclusive or neutral regarding the hypothesis that {hypothesis}. Results are mixed, or the "
        "research does not directly address the proposed relationship, showing a significant gap in the literature. This indicates a need "
        "for more targeted, high-quality research to clarify the proposed interactions.\n"
        f"   - 1 Point: There is weak support for the hypothesis that {hypothesis}. Initial findings, possibly from smaller studies or "
        "indirect evidence, suggest a potential relationship, with limitations such as small sample sizes, indirect evidence linkage, or "
        "preliminary methodological concerns. These findings necessitate cautious interpretation and further research for validation.\n"
        f"   - 2 Points: Strong support exists for the hypothesis that {hypothesis}. Evidence from well-conducted research supports the "
        "proposed relationship, showing clear, statistically significant results favoring the hypothesis, with high methodological quality "
        "and minimal biases. This may include studies directly addressing the pathway or converging evidence from related research areas.\n\n"
    )


def skim_old(b_term, a_term, c_term):
    """
    Provides scoring guidelines for assessing the validity of a given hypothesis about mediated relationships,
    with a focus on the indirect relationships between A and B, B and C, and the co-occurrence of A and C.

    Parameters:
    b_term, a_term, c_term (str): Terms representing the entities involved in the hypothesis to be evaluated.

    Returns:
    str: A set of scoring guidelines based on the level of evidence supporting or contradicting the hypothesis, with particular
         emphasis on the indirect relationships and co-occurrence evidence.
    """
    return (
        f"   - -2 Points: Strong evidence directly contradicts the hypothesis or robust evidence is against the "
        f"effectiveness of the indirect relationship between {a_term} and {b_term}, {b_term} and {c_term}, or lack of co-occurrence significance of {a_term} and {c_term} in the context of {b_term}, with rigorous methodological quality.\n"
        f"   - -1 Point: Moderate evidence raises substantial doubts about the hypothesis. Studies typically show weak or "
        f"negative interactions between {a_term} and {b_term}, {b_term} and {c_term}, or insufficient co-occurrence of {a_term} and {c_term} in relevant contexts, albeit with some "
        f"methodological limitations or biases.\n"
        f"   - 0 Points: The evidence is inconclusive, neutral or irrelevant. Research may show mixed results "
        f"for the interactions between {a_term} and {b_term}, {b_term} and {c_term}, or, when present, of {a_term} and {c_term}, indicating gaps in the literature "
        f"and the need for further, targeted research.\n"
        f"   - 1 Point: Initial evidence supports the hypothesis, showing weak to moderate support for the interactions "
        f"between {a_term} and {b_term}, {b_term} and {c_term}, and some evidence of {a_term} and {c_term} co-occurring in meaningful contexts. However, limitations such as small "
        f"sample sizes or preliminary methodological concerns necessitate cautious interpretation.\n"
        f"   - 2 Points: Strong evidence supports the hypothesis, with clear results demonstrating "
        f"effective interactions between {a_term} and {b_term}, {b_term} and {c_term} involving large sample sizes, strong methodology, and, when present, clear support of the {a_term} and {c_term} relationship with respect to the hypothesis. Evidence is of high methodological quality, with minimal biases.\n\n"
    )


def skim_classify():
    return (
        "-2 Points: The hypothesis can very likely be refuted based on the provided texts. There is a substantial amount of evidence in the provided texts that the hypothesis is false.\n"
        "-1 Point: The hypothesis can likely be refuted based on the provided texts. There is some direct or indirect evidence in the provided texts that the hypothesis is false.\n"
        "0 Points: The hypothesis is not supported or directly refuted by the provided texts. There is either no evidence to support or directly refute the hypothesis in the provided texts, or it is inconclusive, neutral, or irrelevant.\n"
        "1 Point: The hypothesis is likely true based on the provided texts. There is some direct or indirect evidence in the provided texts that the hypothesis is true.\n"
        "2 Points: The hypothesis is very likely true based on the provided texts. There is a good amount of direct or indirect evidence in the provided texts that the hypothesis is true.\n"
    )


def biomedical_hypothesis_strength():
    return """
    -3: Strong evidence against the hypothesis
        • Multiple high-quality studies directly contradict the hypothesis
        • Consistent negative findings across different research methodologies
        • Strong mechanistic evidence opposing the hypothesized relationship

    -2: Moderate evidence against the hypothesis
        • Some well-designed studies show contradictory results
        • Negative findings from meta-analyses or systematic reviews
        • Significant theoretical or mechanistic challenges to the hypothesis

    -1: Weak evidence against the hypothesis
        • Limited studies showing negative results
        • Some contradictory findings, but with methodological limitations
        • Minor theoretical inconsistencies with established knowledge

    0: Insufficient evidence or neutral findings
        • Lack of relevant studies or inconclusive results
        • Conflicting evidence with no clear trend
        • Theoretical basis for the hypothesis, but no empirical support

    1: Weak evidence supporting the hypothesis
        • Limited studies showing positive results
        • Some supporting findings, but with methodological limitations
        • Theoretical consistency with related, established concepts

    2: Moderate evidence supporting the hypothesis
        • Multiple studies with consistent positive findings
        • Supportive results from meta-analyses or systematic reviews
        • Clear mechanistic or theoretical support for the hypothesis

    3: Strong evidence supporting the hypothesis
        • Numerous high-quality studies consistently support the hypothesis
        • Robust positive findings across various research methodologies
        • Strong mechanistic evidence and theoretical framework supporting the hypothesis
    """


def skim():
    return """
- **-2 Points:** The hypothesis is **strongly refuted** by the provided texts. There is substantial evidence suggesting the hypothesis is false or that the interactions have the **opposite effect** of what is proposed.
- **-1 Point:** The hypothesis is **likely refuted** based on the provided texts. The evidence leans towards negating the hypothesis or indicates that the interactions may be **detrimental** to the proposed outcome.
- **0 Points:** The hypothesis is **neither strongly supported nor refuted** by the provided texts. The evidence is inconclusive, mixed, lacks sufficient detail, or there is a lack of direct evidence.
- **1 Point:** The hypothesis is **likely true** based on the provided texts. The available evidence is promising, and the interactions appear **beneficial** to the proposed outcome.
- **2 Points:** The hypothesis is **strongly supported** by the provided texts. The evidence is compelling and consistent, indicating that the interactions are **beneficial** and support the proposed outcome.
"""


def skim_finer():
    return (
        "-2.0 Points: The hypothesis can very likely be refuted based on the provided texts. There is substantial evidence in the provided texts that the hypothesis is false.\n"
        "-1.5 Points: The hypothesis can probably be refuted based on the provided texts. There is considerable evidence in the provided texts that the hypothesis is less likely to be true.\n"
        "-1.0 Point: The hypothesis is likely refutable based on the provided texts. There is some direct or indirect evidence in the provided texts that the hypothesis is false.\n"
        "-0.5 Point: The evidence against the hypothesis in the provided texts is slight, but it slightly leans towards refutation rather than support.\n"
        "0 Points: The hypothesis is not supported or directly refuted by the provided texts. There is either no evidence to support or directly refute the hypothesis in the provided texts, or it is inconclusive, neutral, or irrelevant.\n"
        "0.5 Point: The evidence supporting the hypothesis in the provided texts is slight, but it slightly leans towards support rather than refutation.\n"
        "1.0 Point: The hypothesis is likely true based on the provided texts. There is some direct or indirect evidence in the provided texts that the hypothesis is true.\n"
        "1.5 Points: The hypothesis is probably true based on the provided texts. There is considerable evidence in the provided texts that the hypothesis is more likely to be true.\n"
        "2.0 Points: The hypothesis is very likely true based on the provided texts. There is a good amount of direct or indirect evidence in the provided texts that the hypothesis is true.\n"
    )
