The alveolar gas equation: do lessons from WW2 research into high flying pilots provide insights into the adaptation to high altitude flight in birds?
Bibliographic record
Abstract
Abstract Dalton’s law of partial pressures applies equally to birds and mammals so, as gas moves from the nostrils to the smallest gas-diffusion airways, the sequential addition of water vapour and CO2, steadily reduce the partial pressure of O2 (PO2) within the gas mixture. The PO2, at the point of gas exchange, at sea level, will be about 60 mm Hg less than the original PO2 within atmospheric air. As a result, the inspired PO2 is an inaccurate starting point for any model of oxygen transport. In humans, the interactions of gases at the point of diffusion, is described and quantified by the Alveolar Gas Equation (AGE). Its development during WW2, provided valuable insights into human gas exchange and also into the responses to high altitude flight in pilots but, except for an early study of hypoxia in pigeons, the AGE is not mentioned in the avian literature. Even detailed models of oxygen transport in birds omit the effect of CO2 clearance on pulmonary oxygen transfer. This paper develops two related arguments concerning the application of the AGE to birds. The first is that avian blood gas predictions, based on the theory of multicapillary serial arterialization (MSA), are inaccurate because they do not account for the added partial pressure of diffused CO2. The second is that the primary adaptation to hypobaric hypoxia is the same for both classes and consists of defending PaO2 by reducing PaCO2 through increasing hyperventilation. Support for the first is demonstrated by comparing PaO2 predictions made using the AGE, with published values from avian studies and also against values predicted by the theory of MSA. The second is illustrated by comparing the results of high altitude studies of both birds and humans. The application of the AGE to avian respiratory physiology would improve the predictive accuracy of models of the O2 cascade and would also provide better insights into the primary adaptation to high altitude flight.
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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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".