Recruitment and survival of Northeast Pacific Ocean fish stocks: temporal trends, covariation, and regime shifts
Bibliographic record
Abstract
Two measures of productivity for fish stocks (recruitment and stock–recruit residuals) within two large marine ecosystems (Gulf of Alaska and eastern Bering Sea – Aleutian Islands) showed significant positive covariation within several groups of species and significant negative covariation between certain others. For example, stock–recruit residuals of gadids (Gadidae) in the Bering Sea were inversely related to those of shelf flatfishes (Pleuronectidae), suggesting that environmental forcing affects these groups in opposite ways. Salmon (Oncorhynchus spp.), Pacific herring (Clupea pallasii), and groundfish stocks each showed strong patterns of covariation within these taxonomic groups and within ecosystems, and both salmon and groundfish stocks showed positive covariation between the two ecosystems. However, we found little evidence of covariation between salmon and herring stocks or between these stocks and demersal stocks. Recruitment and stock–recruit residuals in individual stocks did not show a consistent response to known climatic regime shifts. However, combined indices of productivity across stocks showed decadal-scale variability (regime-like patterns), suggesting that both pelagic productivity (mostly salmon) and demersal productivity increased in response to the well-documented 1976–1977 climatic regime shift, whereas the 1988–1989 regime shift produced inconsistent or short-lived responses.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".