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Record W2748830422 · doi:10.1242/jeb.164491

Circulatory mechanisms underlying adaptive increases in thermogenic capacity in high-altitude deer mice

2017· article· en· W2748830422 on OpenAlexafffund
Kevin B. Tate, Catherine M. Ivy, Jonathan P. Velotta, Jay F. Storz, Grant B. McClelland, Zachary A. Cheviron, Graham R. Scott

Bibliographic record

VenueJournal of Experimental Biology · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHigh Altitude and Hypoxia
Canadian institutionsMcMaster University
FundersNational Heart, Lung, and Blood InstituteNatural Sciences and Engineering Research Council of CanadaNational Science Foundation
KeywordsEffects of high altitude on humansAcclimatizationHypoxia (environmental)ThermoregulationBiologyPeromyscusCirculatory systemAerobic capacityInternal medicineCardiac outputVentricleAnimal scienceEndocrinologyEcologyOxygenAnatomyChemistryBlood pressureMedicine

Abstract

fetched live from OpenAlex

We examined the circulatory mechanisms underlying adaptive increases in thermogenic capacity in deer mice (Peromyscus maniculatus) native to the cold hypoxic environment at high altitudes. Deer mice from high- and low-altitude populations were born and raised in captivity to adulthood, and then acclimated to normoxia or hypobaric hypoxia (simulating hypoxia at ∼4300 m). Thermogenic capacity (maximal O2 consumption, VO2max, during cold exposure) was measured in hypoxia, along with arterial O2 saturation (SaO2) and heart rate (fH). Hypoxia acclimation increased VO2max by a greater magnitude in highlanders than in lowlanders. Highlanders also had higher SaO2 and extracted more O2 from the blood per heartbeat (O2 pulse=VO2max/fH). Hypoxia acclimation increased fH, O2 pulse, and capillary density in the left ventricle of the heart. Our results suggest that adaptive increases in thermogenic capacity involve integrated functional changes across the O2 cascade that augment O2 circulation and extraction from the blood.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.106
Threshold uncertainty score0.544

Codex and Gemma teacher scores by category

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

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 head0.051
GPT teacher head0.303
Teacher spread0.253 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations80
Published2017
Admission routes2
Has abstractyes

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