Optimal foraging theory predicts diving and feeding strategies of the largest marine predator
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Accurate predictions of predator behavior remain elusive in natural settings. Optimal foraging theory predicts that breath-hold divers should adjust time allocation within their dives to the distance separating prey from the surface. Quantitative tests of these models have been hampered by the difficulty of documenting underwater feeding behavior and the lack of systems, experimental or natural, in which prey depth varies over a large range. We tested these predictions on blue whales (Balaenoptera musculus), which track the diel vertical migration of their prey. A model using simple allometric arguments successfully predicted diving behavior measured with data loggers. Foraging times within each dive increased to compensate longer transit times and optimize resource acquisition. Shallow dives were short and yielded the highest feeding rates, explaining why feeding activity was more intense at night. An optimal framework thus provides powerful tools to predict the behavior of free-ranging marine predators and inform conservation studies.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 it