The roles of biological reference points and operational control points in management procedures for the sablefish (<i>Anoplopoma fimbria</i>) fishery in British Columbia, Canada
Bibliographic record
Abstract
SUMMARY Biological reference points (BRPs) in fisheries policy are typically sensitive to stock assessment model assumptions, thus increasing uncertainty in harvest decision-making and potentially blocking adoption of precautionary harvest policies. A collaborative management strategy evaluation approach and closed-loop simulation modelling was used to evaluate expected fishery economic and conservation performance of the sablefish (Anoplopoma fimbria) fishery in British Columbia (Canada), in the presence of uncertainty about BRPs. Comparison of models derived using two precautionary harvest control rules, which each complied with biological conservation objectives and short-term economic objectives given by industry, suggested that both rules were likely to avert biomass decline below limit BRPs, even when stock biomass and production were persistently overestimated by stock assessment models. The slightly less conservative, industry-preferred harvest control rule also avoided short-term economic losses of c. CAN$ 2.7–10 million annually, or 10–50% of current landed value. Distinguishing between the role of BRPs in setting fishery conservation objectives and operational control points that define harvest control rules improved the flexibility of the sablefish management system, and has led to adoption of precautionary management procedures.
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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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 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".