Capacity management, not stock status or economics, drives fleet dynamics in the Bay of Biscay ecosystem on a decadal time scale
Bibliographic record
Abstract
This paper introduces a semiqualitative approach to analyse the joint dynamics of fleets and stocks in a multispecies, multifleet fishery. We ask whether changes in fleets affect resource dynamics and whether trends in resource influence fleet dynamics more than external drivers do. External drivers include vessel buyback, fuel price, and fish prices, as well as environmental fluctuations. Resource status is measured by abundance and length metrics; fleet capacity is measured by total horse power, and economic metrics such as profitability and earnings are examined as well. A maximum likelihood approach is used to identify the combined metric trends with the largest support in the data. The approach is applied to the French Bay of Biscay fisheries in 2000–2007. Combined-metric time trends suggest that decreases in fleet capacity did not result in decreasing fishing impacts; trends in stocks and fish prices were not the major drivers of changes in fleets either. Rather, the vessel buyback program might have been the main factor determining fleet dynamics over that period.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".