Temperature dependence of Pacific sardine (<i>Sardinops sagax</i>) recruitment in the California Current Ecosystem revisited and revised
Bibliographic record
Abstract
Small pelagic fish typically show highly variable population dynamics due, in large part, to climate variability. Despite this sensitivity to climate, few stocks of pelagic species are managed with consideration of the environment. The Pacific sardine (Sardinops sagax) represents a notable exception, for which sea surface temperature (SST) from the Scripps Institution of Oceanography (SIO) pier has been used, until recently, to adjust exploitation pressure under warm (favorable) and cold (unfavorable) climate conditions. Recently, the previously established temperature–recruitment relationship was reassessed using different methods, resulting in abandonment of the temperature-sensitive harvest control rule in 2012. In this study, we revisit the previous temperature–recruitment relationship using the original methodology and an updated data set from 1981 to 2010. In contrast to the recent reassessment, we find temperature explains significant variability in recruitment and recruitment success. We also show that mean annual SST averaged over the present California Cooperative Oceanic Fisheries Investigations area is a better predictor of recruitment variability than SST at the SIO pier. We propose that sustainable management of the Pacific sardine should consider climate variability and that the basis for this be periodically updated and revised to inform management with the best available science.
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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 | 0.003 |
| 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 | 0.000 |
| 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".