REDUCTIONS IN OXYGEN CONSUMPTION DURING DIVES AND ESTIMATED SUBMERGENCE LIMITATIONS OF STELLER SEA LIONS (<i>EUMETOPIAS JUBATUS</i>)
Bibliographic record
Abstract
Abstract Accurate estimates of diving metabolic rate are central to assessing the energy needs of marine mammals. To circumvent some of the limitations inherent with conducting energy studies in both the wild and captivity, we measured diving oxygen consumption of two trained Steller sea lions (Eumetopias jubatus) in the open ocean. The animals dived to predetermined depths (5–30 m) for controlled periods of time (50–200 s). Rates of oxygen consumption were measured using open‐circuit respirometry before and after each dive. Mean resting rates of oxygen consumption prior to the dives were 1.34 (±0.18) and 1.95 (±0.19) liter/min for individual sea lions. Mean rates of oxygen consumption during the dives were 0.71 (±0.24) and 1.10 (±0.39) liter/min, respectively. Overall, rates of oxygen consumption during dives were significantly lower (45% and 41%) than the corresponding rates measured before dives. These results provide the first estimates of diving oxygen consumption rate for Steller sea lions and show that this species can exhibit a marked decrease in oxygen consumption relative to surface rates while submerged. This has important consequences in the evaluation of physiological limitations associated with diving such as dive duration and subsequent interpretations of diving behavior in the wild.
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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 | 0.000 | 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) | 0.001 | 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".