Analysis of foraging movements of Atlantic bluefin tuna ( <i>Thunnus thynnus</i> ): individuals switch between two modes of search behaviour
Bibliographic record
Abstract
Abstract We investigate the application of quantitative techniques for distinguishing adaptive search behaviour in Atlantic bluefin tuna ( Thunnus thynnus ). The analysis demonstrates the application of a novel spectral analysis technique for resolving and measuring periodicity in animal behaviour patterns. Two different search strategies are identified that include regulation of turning ( klinokinesis ) and speed ( orthokinesis ). Our results provide evidence that bluefin tuna attempt to optimize their searching efficiency through adjustments in the duration and timing of switching between these two searching strategies. Repetitive, diurnal deep dives were also found to coincide with switching of search behaviour. Additional tracking experiments with larger sample sizes are needed to better identify how individuals switch between the two search strategies and how such decisions may collectively improve the searching and foraging efficiency of their schools ( synchrokinesis , social taxis ) in response to changes in the size or composition of prey aggregations.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".