Testing predictions of optimal diving theory using animal-borne video from harbour seals (<i>Phoca vitulina concolor</i>)
Bibliographic record
Abstract
Optimal diving theory predicts that animals make decisions that maximize their foraging profitability subject to the constraint of oxygen stores. We examined the temporal pattern of prey encounters within a dive from concurrently collected dive data and animal-borne video from a free-ranging pinniped to test predictions of optimal diving theory. Crittercams were deployed on 32 adult male harbour seals (Phoca vitulina concolor De Kay, 1842) at Sable Island, Nova Scotia, Canada, for 3 days each. Deployments resulted in approximately 3 h of video per seal and a total of 2275 capture attempts for 1474 prey encounter events recorded. We found support for seven of the nine selected predictions of optimal diving theory. As predicted, prey encounters increased with bottom duration; dive duration increased with dive depth; and travel duration, bottom duration, and percent bottom duration decreased over a wide range of travel durations. Descent duration did increase with dive depth, and seals terminated dives earlier when no prey were encountered and when prey were encountered later in a dive. Contrary to prediction, bottom duration did not increase and then decrease for short travel durations and dives were not terminated earlier when travel durations were short and prey encounter rate was low.
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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 | 0.002 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".