The influence of buoyancy on diving metabolism of Steller sea lions (Eumetopias jubatus)
Bibliographic record
Abstract
Resting (RMR) and diving metabolic rates (DMR, l O 2 ·min −1 ) were measured in 3 female Steller sea lions (body masses, M b ): 135.0, 168.8 and 213.1 kg) in water with and without adjustment in buoyancy to determine if seasonal fluctuations in subcutaneous adipose tissue affect DMR. Total body water was used to assess percent body fat (body condition) and buoyancy for the control condition (B, range: −86N to −51N). Buoyancy was adjusted either positively (B+, range: −62N to −33 N) or negatively (B−, −101N to 64N) from the control condition to investigate the full range of body conditions experienced in the wild (12–27% body fat). Mean RMR ranged between 1.65 to 1.12 l O 2 · min −1 and was positively correlated with M b (P < 0.01). DMR was corrected for M b (sDMR) using a mass exponent of 0.6 (McPhee et al., J Exp Biol 2002). sDMR was found to decline exponentially with dive duration (DD, min), although significant differences were observed in slope and intercept (P < 0.01) between animals (r 2 = 0.51, ANCOVA). The best mixed model ANOVA, including animal as a random factor, was: urn:x-wiley:08926638:media:fsb2fasebj215a593d:fsb2fasebj215a593d-math-0001 Consequently, sDMR was unaffected by changes in buoyancy but increased with depth and decreased with DD. (Support: North Pacific Marine Science Foundation and NOAA)
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".