Validation of a feedback harvest control rule in data-limited conditions for managing multispecies fisheries
Bibliographic record
Abstract
Harvest control rules (HCRs) for sustainable fishery management have been developed for data-limited fish species for which stock assessments cannot be conducted. However, HCRs have largely not considered mixed-species catches, as when fishing-effort data are widely pooled for numerous minor species in a multispecies fishery. Presently, a feedback HCR has been successfully applied in Japanese fisheries management. By combining management strategy evaluation with a simulation to generate mixed-species data from a multispecies fishery that assume constant catchability (q) among species, we evaluated the performance of this feedback HCR and then compared its performance using species-specific data. In most cases, the biomass was controlled over that needed for maximum sustainable yield (MSY), and the fishing effort was under the fishing mortality consistent with achieving MSY (FMSY). However, for slow-growing species, the biomass might become lower than what is required to remain capable of producing MSY, even though fishing effort was controlled under FMSY. The results show that the feedback HCR is appropriate for multispecies fisheries management where only mixed-species data are available but with special monitoring for slow-growing minor species.
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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 | 0.010 | 0.030 |
| 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 | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".