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Record W3024465586 · doi:10.1101/2020.05.13.093740

The alveolar gas equation: do lessons from WW2 research into high flying pilots provide insights into the adaptation to high altitude flight in birds?

2020· preprint· en· W3024465586 on OpenAlexaff
Michael Seear

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2020
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHigh Altitude and Hypoxia
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsPartial pressureHyperventilationHypoxia (environmental)Effects of high altitude on humansAltitude (triangle)OxygenHypobaric chamberOxygen transportDiffusionAdaptation (eye)ChemistryAtmospheric sciencesThermodynamicsMeteorologyBiologyMathematicsAnesthesiaPhysicsMedicine

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.043
GPT teacher head0.291
Teacher spread0.247 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2020
Admission routes1
Has abstractyes

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