Using willingness‐to‐pay to assess the economic value of weather forecasts for multiple commercial sectors
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 paper uses an alternative to the usual cost‐avoidance approach to estimating the value of weather forecast products. Value is estimated via a demand‐based approach based on the willingness to pay of those who use weather forecast services. Contingent valuation is used to estimate the benefits generated by an automated telephone‐answering device that provides weather forecast information to commercial users in the Toronto area of Ontario, Canada. Commercial sectors included in the study are construction, landscaping/snow‐removal businesses, TV and film, recreation and sports, agriculture, hotel and catering, and institutions such as schools and hospitals. Average value per call varied by commercial sector, from $2.17 for agricultural users to $0.60 per call for institutional users, with an overall mean of $1.20 per call. At roughly 13,750,000 commercial calls annually, this would result in an estimate of benefits generated by the service to commercial users of $16,500,000 per year. Copyright © 2003 Royal Meteorological Society
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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 | 0.001 | 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