Decommissioning of Wadden Sea shrimp fishing licences : Impact analysis of management measures on the fishery
Bibliographic record
Abstract
Deze studie is een deelonderzoek van de overkoepelende sociaal-economische impactanalyse visserij.De centrale onderzoeksvraag is: Wat zijn de economische effecten van de 2021 saneringsregeling op de Waddenzee-garnalenvisserijvergunningen op het visserijcluster geweest?Door met kwantitatieve en kwalitatieve dataanalyse en modellering te kijken naar het kortetermijneffect, is er weinig economisch effect van de sanering op het visserij cluster geobserveerd.Door een verhoogde inzet van de resterende vloot, is de inspanning op de Waddenzee niet significant veranderd en werden lagere vangsten door goede garnalenprijzen gecompenseerd met als gevolg een positief economisch resultaat in het jaar na de sanering.Op de lange termijn is de visserij nu beperkt met een aantal vergunningen dat rond de 20% lager is qua aantal actieve schepen, zonder mogelijkheid om het aantal te laten groeien.This study is part of the overarching socio-economic impact analysis of fisheries project.The central research question is: What have been the economic impacts of the 2021 decommissioning scheme on Wadden Sea shrimp fishing licenses on the fishing cluster.By looking at the short-term effect with quantitative and qualitative data analysis and modelling, little economic effect of the decommissioning scheme on the fishing cluster has been observed.By increasing the effort of the remaining fleet, the effort on the Wadden Sea did not change significantly, lower catches were compensated by good shrimp prices with as a result a positive economic result in the year after the decommissioning.In the long term, the fishery is now limited with a number of licences around 20% lower than the prior number of active vessels, with no possibility of increasing again.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".