Evaluating robustness of harvest control rules to climate-driven variability in Pacific sardine recruitment
Bibliographic record
Abstract
Climate-driven changes in ocean temperatures, currents, or plankton dynamics may disrupt pelagic forage fish recruitment. Being responsive to such impacts enables fisheries management to ensure continued sustainable harvest of forage species. We conducted a management strategy evaluation to assess the robustness of current and alternative Pacific sardine harvest control rules under a variety of recruitment scenarios representing potential projections of future climate conditions in the California Current. The current environmentally informed control rule modifies the harvest rate for the northern sardine subpopulation based on average sea surface temperatures measured during California Cooperative Oceanic Fisheries Investigations field cruises. This rule prioritizes catch at intermediate biomass levels but may increase variability in catch and closure frequency compared to alternative control rules, especially if recruitment is unrelated to ocean temperatures. Fishing at maximum sustainable yield and using dynamically estimated reference points reduced the frequency of biomass falling below 150 000 mt by up to 17%, while using survey index-based biomass estimates resulted in a 14% higher risk of delayed fishery closure during stock declines than when using assessment-based estimates.
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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 | 0.018 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".