The ghosts of overfishing past that haunt present day fisheries management
Bibliographic record
Abstract
Fish biomass is the most widely used indicator of fish stock health. Stocks whose biomass is or has previously collapsed owing to overfishing, and the management systems built around them, may carry a memory of decline, even if biomass has recovered. This is because stock biomass as the main indicator of stock health does not represent all aspects of stock health and biomass can possibly become a weaker indicator of health after stock collapse. These latent weaknesses have been termed “ghosts of overfishing past”. Not accounting for ghosts can impact the speed of stock recovery and susceptibility to further collapses. This concept has been popularised by Professors Jeff Hutchings, Anna Kuparinen, and others. Ghosts are varied and can include changes in vital rates, phenotypic response, fish behaviour, and aspects of the human system such as institutional inertia, fisheries subsidies, and income portfolios. The presence of ghosts has implications for fisheries management: altering stock biomass objectives (dynamic reference points) may be appropriate for populations that have experienced collapse even if biomass has recovered. Ghosts should be considered when developing management strategies for populations that have previously experienced large declines.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".