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人工智能在科学测验中的应用

通过问题设计、数据分离、不确定性、来源、跨仪器测试和声明证据来评估科学人工智能。

相关引导路径人工智能科学
问题 1 的 8

Before selecting a scientific AI method, what should a researcher define?

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  1. Before selecting a scientific AI method, what should a researcher define?
    • The scientific question and what observation would disconfirm the claim
    • Only the model architecture
    • The desired publication date
    • A result that cannot be challenged
  2. Why might a random train/test split overstate generalization when readings are correlated?
    • The test set becomes larger
    • Data from the same subject, instrument, or experiment can appear across splits
    • Random splits remove all uncertainty
    • The model cannot train on numbers
  3. What should a researcher record to support reproducibility?
    • Only the model’s final prediction
    • Only the paper title
    • Preprocessing, model versions, relevant random seeds, and evaluation material
    • The number of plots generated
  4. What is required before treating an AI-generated hypothesis as a supported scientific claim?
    • As soon as the model explains it confidently
    • Whenever it matches a familiar theory
    • Only when the model’s score is high
    • After appropriate experiments or independent analysis support it
  5. A model performs well on random readings from instrument A but poorly on instrument B. What should the team do?
    • Report both results and investigate calibration or measurement differences
    • Report only the better score
    • Claim general performance based on the random split
    • Remove instrument B from the record
  6. Why separate what is measured from what is inferred?
    • It removes the need for replication
    • It clarifies which parts are observations and which are model conclusions
    • It guarantees a causal explanation
    • It makes missing data irrelevant
  7. What should a researcher understand about calibration, confidence intervals, and prediction intervals?
    • They are interchangeable scores
    • Use only the largest value
    • They answer different questions
    • Replace them with the model name
  8. What should be preserved from source data through a published figure?
    • Only a screenshot of the chart
    • The model’s general description
    • A list of software logos
    • The path from source data to figure, table, or manuscript