Maximum wind speeds and US hurricane losses
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
There is academic, commercial, and public interest in estimating loss from hurricanes striking land and understanding how loss might change as a result of future variations in climate. Here we show that the relationship between wind speed and loss is exponential and that loss increases with wind speed at a rate of 5% per m s −1 . The relationship is derived using quantile regression and a data set comprising wind speeds of hurricanes hitting the United States and normalized economic losses. We suggest that the “centercepts” for the different quantiles account for exposure‐related factors such as population density, precipitation, and surface roughness, and that once these effects are accounted for, the increase in loss with wind speed is consistent across quantiles. An out‐of‐sample test of this relationship correctly predicts economic losses from Hurricane Irene in 2011. The exponential relationship suggests that increased wind speeds will produce significantly higher losses; however, increases in exposed property and population are expected to be a more important factor for near future losses.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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