Behaviour and habitat preferences of bigeye tuna (Thunnus obesus) and their influence on longline fishery catches in the western Coral Sea
Bibliographic record
Abstract
Data on the depth and temperature preferences of bigeye tuna ( Thunnus obesus ) derived from archival tags were integrated with data on the spatial and temporal distribution of catches from an eastern Australian longline fishery to investigate the relationship between bigeye tuna behaviour and the fishery. Tagged individuals demonstrated variability in depth and water temperature preferences on diurnal, lunar, and seasonal scales. Deeper, cooler waters were frequented during the day, and shallower, warmer waters were frequented at night, with nighttime preferences often deeper around the full moon, although this was not consistent between individuals or temporally within individuals. Marked individual variability in depth and water temperature preferences suggest bigeye tuna are flexible in foraging strategies utilized, thereby allowing individuals to maximize their ability to successfully forage in a patchy environment. Catches of bigeye tuna corresponded with the spatial and temporal overlap of bigeye tuna distributions within the fishery on similar scales, suggesting clear influence of bigeye tuna behaviour on the behaviour of the fishery and catches. However, variability in these relationships suggests that the factors influencing the relative catchability of bigeye tuna are complex, and there are likely to be a range of additional environmental, behavioural, and operational factors that influence bigeye tuna catchability.
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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".