Bibliographic record
Abstract
The demand for crop hail insurance is examined in both static and dynamic models and both with and without all‐risk crop insurance. Contrary to general results on optimal insurance with background risk, crop revenue uncertainty induces the farmer to decrease rather than increase coverage. The underinsurance results are strengthened when farmers are able to dynamically update their insurance portfolio as information about the value of the crop is revealed over time. When hail insurance is purchased along with all‐risk crop insurance, two alternative approaches are examined and their efficiency properties compared. Nous examinons la demande d'assurance‐grêle au moyen, à lafois, de modèles statiques et de modèles dynamiques, chacun intégré ou non à une assurance tout‐risque. Contrairement à ce qu'on observe généralement pour le niveau d'assurance optimal établi enprésence d'un risque sous‐jacent, l'incertitude quant au rendement des cultures attendu incite l'exploitant agricole à réduire, plutôt qu'à augmenter, sa couverture. Les résultats de cette sous‐assurance sont améliorés lorsque l'exploitant est capable d'actualiser régulièrement son portefeuille d'assurance à mesure que la valeur finale de la récolte se précise. Dans le cas de l'assurance‐grêle achetée dans le cadre d'une assurance‐récolte tout‐risque, nous comparons deux options quant à leur possibilité de rendement économique.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".