Cold lungs, warm heart? Temperature effects on the oxygen‐hemoglobin dissociation curve of bar‐headed geese
Bibliographic record
Abstract
Bar‐headed geese accomplish the extraordinary feat of migrating over the Himalayas, where oxygen (O 2 ) levels are only ½ −1/3 that at sea‐level. It is well known that hemoglobin (Hb) of this species has enhanced O 2 affinity, assisting O 2 loading in this hypoxic environment. As temperature at these altitudes is extremely low and bar‐headed geese cross the mountains when air temperatures are at daily minima, we hypothesized that enhanced temperature effects may be beneficial for O 2 loading (if these birds decrease temperature at the blood/gas barrier) and O 2 unloading at the exercising muscle. Using the mixing technique on whole‐blood, preliminary evidence suggests that the O 2 ‐Hb curve temperature coefficient of bar‐headed geese is higher than that of other birds near baseline CO 2 (4%) and more interestingly, is highest when CO 2 is high (7%) (e.g. at locomotory muscle). This should enhance unloading of O 2 to exercising tissues, where temperature and CO 2 levels will be near their maxima. At low CO 2 (1%) (e.g. as may be experienced at the blood/gas barrier due to the enhanced hypoxic ventilatory response in this species) the temperature coefficient is lowest, suggesting that decreased temperature may not further enhance O 2 loading. This finding parallels that of the diving emperor penguin in its analogous environment, as its air sac temperature is not decreased despite extremely low ambient temperatures. NSF Funded
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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".