Fishing and the impact of marine reserves in a variable environment
Bibliographic record
Abstract
We use discrete-time models to investigate the impact of marine reserve establishment on fishery catch and biomass levels in open-access and quota-regulated fisheries under conditions of recruitment variability and natural mortality events. We find that under the conditions of variability tested, reserves can increase the probability of achieving target levels of biomass (60%, 35%, and 5% of carrying capacity) and can reduce catch variability in neighbouring fisheries, making future planning in the fishery more efficient. The size of the reserve required to meet each objective will depend on the initial condition of the stock and the exploitation rate in the fishery. Reserve coverage of between 20% and 40% prevent stock collapse in most cases. In heavily exploited fisheries, reserves are also likely to enhance mean catches, particularly in highly variable systems. If the stock has previously been heavily exploited, large reserves (≥60%) may be required to significantly increase the probability of achieving target biomass levels. However, once stocks have recovered, reserve coverage may be reduced without a reduction in this probability of success.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".