How useful is the ratio of fish density outside versus inside no-take marine reserves as a metric for fishery management control rules?
Bibliographic record
Abstract
A management strategy evaluation (based on five species in the California, USA, nearshore fishery) of control rules that reduce relative fishing effort as a function of the ratio of fish density outside versus inside no-take marine reserves (as a measure of depletion) showed that although the control rules allowed effort to increase at first, in the long term, they were effective at maintaining spawning stock biomass and yield for all simulated species, including depleted ones. Scenarios with fish movement, illegal fishing in the reserve, or post-dispersal density dependence in recruitment required higher density ratio targets, such as 60% of mature fish or 80% of all fish, to avoid stock depletion. The effort allowed by multispecies density-ratio control rules depended on the relative weight given to more or less depleted species. High variability in recruitment or in monitoring data caused the allowable effort to fluctuate. Density-ratio control rules have the advantages that they require no historical data, they can be used at local spatial scales, and they adjust to changing environmental conditions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".