Cardiovascular adjustments during locomotion in penguins
Bibliographic record
Abstract
The high costs of airborne flapping flight are reflected in a difference between the oxygen pulse (OP; amount of oxygen consumed per heart beat) during flight and that during terrestrial locomotion, as well as a difference in the relationship between heart rate (fH) and rate of oxygen consumption ([Formula: see text]O2). We tested and failed to accept the hypothesis that there would be similar differences in the fH–[Formula: see text]O2and [Formula: see text]O2–OP relationships during swimming under water and during walking for macaroni penguins (Eudyptes chrysolophus (Brandt, 1837)). We suggest that this may be a result of the penguins having to overcome physical forces different from those experienced by volant birds and possibly a response to limited access to oxygen while porpoising or diving. This result has important implications for those who wish to use fHto estimate [Formula: see text]O2and hence metabolic rate in the field for this and similar species. To test the utility of the relationship, we compared our results with data obtained previously for this species, and we simultaneously measured [Formula: see text]O2in a respirometer and estimated [Formula: see text]O2from fHfor 48 h. There was no significant difference in the fH–[Formula: see text]O2or [Formula: see text]O2–OP relationships between the two studies or between [Formula: see text]O2estimated from fHand that measured using respirometry.
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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 | 0.001 |
| 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".