引导式测验 · 中等 等级
人工智能与气候测验
按任务、地点、范围、不确定性、数据覆盖范围、罕见事件表现、资源使用和决策后果评估气候人工智能。
问题 1 的 8
What information should accompany a climate-related prediction?
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- What information should accompany a climate-related prediction?
- Location, time horizon, uncertainty, and intended decision
- 仅型号名称
- A single number without context
- 训练纪元数
- Why should climate models be tested on unusual events as well as average conditions?
- Average performance guarantees extreme-event performance
- Unusual events should always be removed
- Rare events may matter most for planning even if average error is low
- Rare events have no effect on decisions
- What can measurement changes do to an apparent environmental trend?
- Guarantee the trend is real
- Create an apparent change unrelated to the climate outcome
- Remove missing observations
- Make geographic coverage equal
- How should model performance transfer to a different climate or sensor setup be treated?
- Assume it works if the model is large
- Use the same score without checking
- Reuse the original aggregate score as proof of local performance
- It needs evaluation because transfer is not automatic
- What should a team measure when considering AI’s climate impact?
- Compute use and downstream operational effects under stated assumptions
- 仅参数数量
- Only the model’s predicted score
- 数据点的数量
- What does a model’s compute estimate establish about a full lifecycle impact?
- It equals every downstream effect
- It proves emissions fell
- It is one component, not automatically a complete lifecycle assessment
- It establishes a universal carbon saving
- Why check geographic coverage and measurement quality before using a climate forecast in a community?
- Coverage does not affect climate applications
- The available observations and evaluation may not represent that location or its conditions
- It guarantees every location has sensors
- It removes the need for uncertainty
- Who should retain authority over environmental or emergency actions?
- The model automatically
- The sensor vendor
- 训练数据集
- The accountable decision-maker using evidence and review