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Ventilatory Responses to Changes in Temperature in Mammals and Other Vertebrates

2000· review· en· W2137249010 on OpenAlexaff
Jacopo P. Mortola, Peter B. Frappell

Bibliographic record

VenueAnnual Review of Physiology · 2000
Typereview
Languageen
FieldNeuroscience
TopicNeuroscience of respiration and sleep
Canadian institutionsMcGill University
Fundersnot available
KeywordsEctothermHomeothermyTorporHypercapniaHypoxia (environmental)Control of respirationBiologyMetabolic control analysisRespirationMetabolic rateThermoregulationVentilation (architecture)EcologyRespiratory systemOxygenChemistryEndocrinologyAnatomyThermodynamicsInsulin

Abstract

fetched live from OpenAlex

This article reviews the relationship between pulmonary ventilation (VE) and metabolic rate (oxygen consumption) during changes in ambient temperature. The main focus is on mammals, although for comparative purposes the VE responses of ectothermic vertebrates are also discussed. First, the effects of temperature on pulmonary mechanics, chemoreceptors, and airway receptors are summarized. Then we review the main VE responses to cold and warm stimuli and their interaction with exercise, hypoxia, or hypercapnia. In these cases, mammals attempt to maintain both oxygenation and body temperature, although conflicts can arise because of the respiratory heat loss associated with the increase in ventilation. Finally, we consider the VE responses of mammals when body temperature changes, as during torpor, fever, sleep, and hypothermia. In ectotherms, during changes in temperature, VE control becomes part of a general strategy to maintain constant relative alkalinity and ensure a constancy of pH-dependent protein functions (alphastat regulation). In mammals on the other hand, VE control is aimed to balance metabolic needs with homeothermy. Therefore, alphastat regulation in mammals seems to have a low priority, and it may be adopted only in exceptional cases.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.048
GPT teacher head0.366
Teacher spread0.319 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations91
Published2000
Admission routes1
Has abstractyes

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