Somatic growth of Atlantic bluefin tuna (<i>Thunnus thynnus</i>) under global climate variability: evidence from over 60 years of daily resolved growth increments with a simulation study
Bibliographic record
Abstract
Somatic growth is integral to fishery stock productivity. Under climate variability, omitting growth variability renders fishery management strategies non-optimal. Based on a multidecadal tag–recapture database, a case study is presented to investigate the potential growth response of the Atlantic bluefin tuna (Thunnus thynnus) to three regionally relevant large-scale climate patterns: the North Atlantic Oscillation, Arctic Oscillation, and Pacific North America pattern. An additional simulation study is conducted to explore the effect of the overall scale and the distribution of measurement error on the detection probability of extrinsic effects and the estimation of growth parameters. Results indicate significant growth response at an intra-annual scale to all three climate indices examined. Identified growth responses to climate variations are highly nonlinear. The projected growth shows increased growth in recent decades under climate variability with respect to the historical mean. Simulation results show a higher probability to detect climate signals when the overall measurement error is low. Substantial bias is expected when the measurement error at tag release is high, cautioning against careless integration of different types of growth data.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".