A test of hypotheses based on optimal foraging considerations for a diving mammal using a novel experimental approach
Bibliographic record
Abstract
The response of marine predators to changes in fine-scale prey distribution is poorly understood. Precipitous declines in marine apex predators necessitate a better understanding of the magnitude of fluctuations in prey availability that are within the compensatory behavioural plasticity of predators. We experimentally manipulated the fine-scale prey field for a marine carnivore in a controlled, captive setting and examined changes in behaviour and efficiency with changes in prey encounter rate. We hypothesized (i) a minimum prey encounter rate below which the cost of foraging always exceeds the benefit, (ii) foraging effort should increase with increasing prey encounter rates, and (iii) a maximum threshold prey encounter rate at which foraging efficiency is optimized. Dive duration, foraging time, and dive and foraging efficiency increased significantly with increasing prey encounter rate up to an asymptote of ~13 fish per dive cycle, supporting two of the three hypotheses. The results also support predicted responses to changing prey encounter rates derived from an optimal foraging model for diving animals and are the first experimental validation of optimal foraging model predictions in a marine mammal. We believe that these results provide new insight and suggest new experimental techniques for examining the foraging ecology of large marine predators.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".