Dive behaviour impacts the ability of heart rate to predict oxygen consumption in Steller sea lions (<i>Eumetopias jubatus</i>) foraging at depth
Bibliographic record
Abstract
The predictive relationship between heart rate (f(H)) and oxygen consumption (VO2) has been derived for several species of marine mammals swimming horizontally or diving in tanks to shallow depths. However, it is unclear how dive activity affects the f(H):VO2 relationship and whether the existing equations apply to animals diving to deeper depths. We investigated these questions by simultaneously measuring the f(H) and VO2 of Steller sea lions (Eumetopias jubatus) under different activity states (surface resting or diving), types of dives (single dives or dive bouts), and depths (10 or 40 m). We examined the relationship over dives only and also over dive cycles (dive + surface interval). We found that f(H) could only predict VO2 over a complete single dive cycle or dive bout cycle (i.e. surface intervals had to be included). The predictive equation derived for sea lions resting on the surface did not differ from that for single dive cycles. However, the equation derived over dive bout cycles (multiple dives + surface intervals) differed from those for single dive cycles or surface resting, with similar f(H) for multiple dive bout equations yielding higher predicted VO2 than that for single dive bout cycles (or resting). The f(H):VO2 relationships were not significantly affected by dive duration, dive depth, water temperature or cumulative food consumed under the conditions tested. Ultimately, our results demonstrate that f(H) can be used to predict activity-specific metabolic rates of diving Steller sea lions, but only over complete dive cycles that include a post-dive surface recovery period.
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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) | 0.000 | 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".