Source water variability as a driver of rockfish recruitment in the California Current Ecosystem: implications for climate change and fisheries management
Bibliographic record
Abstract
Elucidating connections between ocean climate variability and change and recruitment of juvenile fishes to adult populations is critical for understanding variability in stock–recruit dynamics. Recruitment to adult rockfish populations in the California Current Ecosystem (CCE) is highly variable, leading to short- and long-term changes in abundance, productivity, forage availability, and potential fisheries yield. We used regional ocean model output, oceanographic data, and a 34-year time series of pelagic juvenile rockfish to investigate the interaction between changes in CCE source waters, as reflected by physical water mass properties, and recruitment variability. Specifically, variability of “spiciness” on upper water isopycnals explains a substantial fraction of the variation in pelagic juvenile rockfish abundance. High rockfish abundances correspond to cooler, fresher waters with higher dissolved oxygen (i.e., “minty”) conditions, indicative of Pacific subarctic water. By contrast, years of low rockfish abundance are associated with warmer, more saline, and more oxygen-deficient (i.e., “spicy”) conditions, reflecting waters of subtropical or equatorial origin. Transport and source waters in the CCE are key factors determining density-independent processes and subsequent recruitment to adult populations.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".