Evaluatie Regeling brede weersverzekering
Bibliographic record
Abstract
In de open teelten van de agrarische sector zijn er weinig mogelijkheden om risico's die weersinvloeden kunnen hebben op de productie te voorkomen.Schade als gevolg van een extreme weersomstandigheid (zoals hevige regenval of hagel) kan echter een grote invloed hebben op de financiële positie van de bedrijven en de sector als geheel.Vóór 2002 sprong de overheid geregeld bij in het vergoeden van schade als gevolg van extreme weersomstandigheden.Om een commerciële markt te creëren stimuleert de overheid tegenwoordig via een premiesubsidie deelname aan een brede weersverzekering.Dit rapport evalueert ten eerste of deze premiesubsidie heeft geleid tot een commercieel aantrekkelijke verzekering en ten tweede of deze subsidie ertoe heeft geleid dat het aantal en de omvang van de verzoeken tot schadevergoeding is verminderd.Het antwoord op de eerste vraag is nee; het antwoord op de tweede vraag is moeilijk objectief vast te stellen op basis van empirische gegevens.The agricultural sector has limited opportunities to avoid risks of weather events for open field crops.However, damage caused by extreme weather conditions (such as heavy rain or hail) can have a major impact on the financial position of the farms and the whole agricultural sector.Before 2002 the Dutch government regularly compensated damage caused by extreme weather events.Nowadays the government encourages farmers to participate in a weather insurance scheme by granting a premium subsidy, thus trying to create a commercial market for weather insurances.This report evaluates the premium subsidy.The core questions are first if the subsidy has led to a commercially attractive weather insurance scheme and second if the premium subsidy has reduced the number of requests for compensation and the amount.The answer to the first question is no; due to a lack of empirical evidence, there is no clear objective answer to the second question.
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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 | 0.019 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.004 |
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".