A systemic approach to analyzing post-collapse adaptations in the Bay of Biscay anchovy fishery
Bibliographic record
Abstract
The Bay of Biscay anchovy fishery system has undergone important transformations following a closure from 2005 to 2010. Through a multidisciplinary and systemic approach, combining analyses of fisheries and market data with interviews with key stakeholders, we analyze adaptive responses of the main system components in France and Spain, considering how the fishing sector and fishery management institutions have adapted to changes. Focusing on the question “what has been lost and gained following the collapse?”, we find that while the anchovy stock has recovered, the fishery system has not returned to its pre-collapse status with important socio-economic features having been lost. We highlight the need for holistic consideration of multiple system components and diverse stakeholders’ perspectives. The perceived losses and gains from the anchovy fishery collapse and aftermath are found to vary across the players in the fishery system, depending as well on the management objectives and scales being considered. Such retrospective analysis can serve as a basis for understanding the long-term responses to social-ecological changes in fisheries and identifying the role of governance mechanisms in supporting adaptations that maintain sustainable fishery systems in the face of future potential shocks.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".