Economic Value of Weather and Climate Forecasts
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract This article, which deals with methods for quantifying the economic value of weather and climate forecasts, is organized as follows. Section 2 provides some background on methods used to produce weather and climate forecasts, including the distinction between “weather” and “climate.” Section 3 introduces the concept of the economic value of imperfect information, based on the framework of decision theory and expected utility maximization. Section 4 reviews specific decision-analytic studies of the economic value of weather and climate forecasts. As a complement to the decision-theoretic approach, nonmarket valuation of weather and climate forecasts based on stated preference methods are described in Section 5. As an example, a recent survey of the public to obtain willingness-to-pay estimates for the economic value of improved hurricane forecasts is treated in detail. Finally, Section 6 consists of a discussion focusing on future research directions that could result in improved assessment of the economic value of weather and climate forecasts.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it