Performance evaluation of harvest control rules for Pacific herring management in British Columbia, Canada
Bibliographic record
Abstract
Abstract Cleary, J. S., Cox, S. P., and Schweigert, J. F. 2010. Performance evaluation of harvest control rules for Pacific herring management in British Columbia, Canada. – ICES Journal of Marine Science, 67: 2005–2011. Despite application of a harvest control rule (HCR) since 1986, abundance of several Pacific herring (Clupea pallasi) stocks in British Columbia, Canada, are currently below levels considered adequate for exploitation. An alternative HCR, based on default limit and upper stock reference (USR) points at 0.4 BMSY and 0.8 BMSY, was recently developed under Canada's precautionary fisheries management policy. We simulated the Pacific herring fishery management system to examine whether (i) realized fishery performance over the past 10 years is an expected consequence of applying the existing herring HCR (with a single lower reference point) and (ii) performance could be improved by adopting the Department of Fisheries and Oceans new HCR with limit and USR points. Both HCRs successfully rebuilt stocks to sustainable levels under a high-productivity scenario, but performed poorly when stock productivity was low. The two HCRs were sensitive to stock productivity, because the effect of a target harvest rate (20%) that is independent of productivity was much larger than the effects of biomass reference-point choices. We therefore recommend further research on estimating reference points and sustainable harvest rates for Pacific herring, so that HCRs may be made more responsive to changes in productivity.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 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".