An ecosystem-based hypothesis for climatic effects on surplus production in California sardine (<i>Sardinops sagax</i>) and environmentally dependent surplus production models
Bibliographic record
Abstract
We used environmentally dependent surplus production (EDSP) models to test hypotheses linking changes in habitat area, carrying capacity and surplus production in California sardine (Sardinops sagax). Habitat area (with mean sea surface temperatures of 1416 °C) was centered off Oregon, Washington, and British Columbia during JulyDecember and off southern and central California during JanuaryJune. Habitat area increased during El Niño and decreased during La Niña events. EDSP models fit better than a conventional Fox surplus production model without environmental data. Our estimated fishing mortality rate at maximum sustained yield FMSY = 0.099·year1 was consistent with other estimates. Maximum sustained yield (MSY) and stock biomass for MSY (BMSY) depend on habitat area and environmental conditions. Negative surplus production occurred when biomass was high and habitat area declined abruptly. Managers might monitor habitat area to anticipate changes in the California sardine stock and changes in the California Current ecosystem. Periods of high productivity appear easier to identify than periods of negative productivity. Models that incorporate environmental effects on both recruitment and survival and mortality of adult fish appear useful in studying climatic effects on fishery surplus production.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".