Influence of oceanographic variability on recruitment of yellowfin tuna (Thunnus albacares) in the western and central Pacific Ocean
Bibliographic record
Abstract
Recruitment estimates for yellowfin tuna ( Thunnus albacares ) in the western and central Pacific Ocean (WCPO), derived from a stock assessment model, are highly variable seasonally, interannually, and over decadal periods. A generalized linear model (GLM) was developed that predicts the variation in yellowfin tuna recruitment in response to a range of oceanographic variables. The GLM model accounted for 54% of the variation in quarterly recruitment for the period 1980–2003, with the inclusion of seven different oceanographic variables derived from a zone within the northwestern equatorial region of the WCPO. The robustness of the recruitment model was investigated by cross-validation. The GLM was complemented by a cluster analysis approach that identified five principal oceanographic states within the northwestern zone selected by the GLM. Incorporation of the recent GLM recruitment indices in the yellowfin tuna stock assessment model is likely to improve the precision of estimates of current and projected (next 1–2 years) biomass and exploitation rates. In a broader context, the recruitment model provides a tool to investigate how yellowfin tuna recruitment might vary in response to short- and long-term variation in the oceanographic conditions of the WCPO.
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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 | 0.004 |
| 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) | 0.000 | 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".