Contrasting effects of coastal upwelling on growth and recruitment of nearshore Pacific rockfishes (genus <i>Sebastes</i>)
Bibliographic record
Abstract
Knowledge of processes underlying recruitment is critical for understanding marine population dynamics and their response to ocean climate. We investigated the relationship between coastal upwelling and early life history of black rockfish (Sebastes melanops), a midwater aggregating species, and CQB rockfishes (a solitary benthic species complex including Sebastes caurinus, Sebastes maliger, and Sebastes auriculatus), between two oceanographically distinct years on the west coast of Vancouver Island, Canada. We analysed otolith microstructure to determine parturition and settlement dates, pelagic durations, and pre- and postsettlement growth rates. High CQB rockfish recruitment in 2005 was associated with prolonged downwelling and warm ocean temperatures, late parturition dates, fast presettlement growth, short pelagic durations, and small size-at-settlement. In contrast, high black rockfish recruitment in 2006 was associated with strong upwelling and cool ocean temperatures, slow presettlement growth, and protracted pelagic durations. Presettlement growth of both rockfish complexes increased with high sea surface temperature, but was unrelated to chlorophyll a concentration. Our results indicate that the same oceanographic conditions give rise to fast presettlement growth and short pelagic durations for both groups, but that different factors lead to strong recruitment in each.
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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 | 0.000 | 0.001 |
| 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".