To what extent might N2 limit dive performance in king penguins?
Bibliographic record
Abstract
A mathematical model was used to explore if elevated levels of N2, and risk of decompression sickness (DCS), could limit dive performance (duration and depth) in king penguins (Aptenodytes patagonicus). The model allowed prediction of blood and tissue (central circulation, muscle, brain and fat) N2 tensions (P(N2)) based on different cardiac outputs and blood flow distributions. Estimated mixed venous P(N2) agreed with values observed during forced dives in a compression chamber used to validate the assumptions of the model. During bouts of foraging dives, estimated mixed venous and tissue P(N2) increased as the bout progressed. Estimated mean maximum mixed venous P(N2) upon return to the surface after a dive was 4.56+/-0.18 atmospheres absolute (ATA; range: 4.37-4.78 ATA). This is equivalent to N2 levels causing a 50% DCS incidence in terrestrial animals of similar mass. Bout termination events were not associated with extreme mixed venous N2 levels. Fat P(N2) was positively correlated with bout duration and the highest estimated fat P(N2) occurred at the end of a dive bout. The model suggested that short and shallow dives occurring between dive bouts help to reduce supersaturation and thereby DCS risk. Furthermore, adipose tissue could also help reduce DCS risk during the first few dives in a bout by functioning as a sink to buffer extreme levels of N2.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".