Stage‐specific drivers of Pacific hake (<scp><i>Merluccius productus</i></scp>) recruitment in the California Current Ecosystem
Bibliographic record
Abstract
Abstract Understanding environmental drivers of recruitment variability in marine fishes remains an important challenge in fish ecology and fisheries management. We developed a conceptual life‐history model for Pacific hake ( Merluccius productus ) along the west coast of the United States and Canada to generate stage‐specific and spatiotemporally‐specific hypotheses regarding the oceanographic and biological variables that likely influence their recruitment. Our model included seven life stages from pre‐spawning female conditioning through pelagic juvenile recruitment (age‐0 fish) for the coastal Pacific hake stock. Model‐estimated log recruitment deviations from the 2020 hake assessment were used as the dependent variable, with predictor variables drawn primarily from a regional ocean reanalysis for the California Current Ecosystem. Indices of prey and predator abundance were also included in our analysis, as were predictors of local‐ and basin‐scale climate. Five variables explained 59% of the recruitment variability not accounted for by the stock–recruitment relationship in the hake assessment. Recruitment deviations were negatively correlated with May–September eddy kinetic energy between 34.5° and 42.5°N, the North Pacific Current Bifurcation Index, and Pacific herring ( Clupea pallasii ) biomass during the spawner preconditioning stage, alongshore transport during the yolk‐sac larval stage, and the number of days between storm events during the first‐feeding larval stage. Other important predictors included upwelling strength during the preconditioning stage, the number of calm periods during the first‐feeding larval stage, and age‐1 hake predation on age‐0 pelagic juveniles. These findings suggest that multiple mechanisms affect Pacific hake survival across different life stages, leading to variability in population‐level recruitment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".