A hurricane wind risk and loss assessment of Caribbean agriculture
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Hurricanes act as large external shocks potentially causing considerable damage to agriculture in the Caribbean. While a number of studies have estimated their historic economic impact, arguably the wider community and policy makers are more concerned about their future risk and potential losses, since this type of information is useful for disaster preparedness and mitigation strategy and policy. This paper implements a new approach to undertaking a quantitative wind risk and loss assessment of agriculture in Caribbean island economies. The authors construct an expected loss function that uses synthetically generated, and historical, hurricane tracks within a wind field model that takes cropland exposure derived from satellite data into consideration. The results indicate that expected wind losses are potentially large but vary considerably across the region, where the smaller islands are considerably more likely to be negatively impacted. Moreover, we find that the structure of the agricultural sector can be important in terms of vulnerability.
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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) | 0.001 | 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