Hierarchical interpretation of nonlinear relationships linking yellowfin tuna (<i>Thunnus albacares</i>) distribution to the environment in the Atlantic Ocean
Bibliographic record
Abstract
Using generalized additive models, we show evidence for nonlinear relationships between various hydrological factors and age-structured catch per unit effort of Atlantic yellowfin tuna (Thunnus albacares) for two fishing fleets. Catchability effects are distinguished from tuna environmental preference effects in the catch per unit effort variability. With respect to catchability, an important nonlinear effect of local fishing effort is highlighted for each fleet. It is interpreted as resulting from a local overfishing phenomenon of adult yellowfin tuna and from vessel fishing tactics (cooperation/spying). The environmental preferences obtained facilitate the interpretation of the hierarchical spatial distribution and age-dependent movements of the yellowfin population. We show that, on a large spatiotemporal scale (the whole ocean), low salinity is a good predictor of yellowfin habitat. Juveniles are mainly distributed in low-salinity waters (<0.035 kg·kg1) when adults extend their range to waters of 0.036 kg·kg1. On a mesoscale, adult population annual reproductive transatlantic displacements are probably driven by temperature and salinity gradients to warm and low-salinity locations that are favorable for juveniles. Northsouth seasonal movements of the population are clearly related to warmwater seasonal oscillations. On a small scale, ocean thermic stability and gradients of sea surface temperature are important physical factors determining yellowfin concentration.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".