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Acid-base balance at high altitude in lowlanders and indigenous highlanders

2022· article· en· W4205860068 on OpenAlexafffund
Michael M. Tymko, Christopher K. Willie, Connor A. Howe, Ryan L. Hoiland, Rachel Stone, Kaitlyn Tymko, Courtney Tymko, David B. MacLeod, James D. Anholm, Christopher Gasho, Francisco C. Villafuerte, Gustavo Vizcardo‐Galindo, Rómulo Figueroa‐Mujíca, Trevor A. Day, Jordan D. Bird, Glen E. Foster, Craig D. Steinback, Julien V. Brugniaux, Benoît Champigneulle, Émeric Stauffer, Stéphane Doutreleau, Samuel Vergès, Erik R. Swenson, Philip N. Ainslie

Bibliographic record

VenueJournal of Applied Physiology · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHigh Altitude and Hypoxia
Canadian institutionsUniversity of ManitobaUniversity of WindsorVancouver General HospitalUniversity of AlbertaMount Royal UniversityOkanagan University CollegeUniversity of British Columbia, Okanagan CampusInternational Collaboration On Repair DiscoveriesUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsGovernment of Canada
KeywordsEffects of high altitude on humansAcclimatizationBiologyAcid–base homeostasisAltitude (triangle)Respiratory compensationAnimal sciencePhysiologyEndocrinologyEcologyAnaerobic exerciseAnatomy

Abstract

fetched live from OpenAlex

Lowlander, Andean, and Sherpa arterial blood data were combined across five independent high-altitude expeditions in the United States, Nepal, and Peru to assess acid-base status at ∼3,800, ∼4,300, and ∼5,000 m. The main finding was that Andean and Sherpa highlander populations have more acidic arterial blood, due to elevated arterial carbon dioxide and similar arterial bicarbonate compared with acclimatizing lowlanders at altitudes ≥4,300 m.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.030
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.005
GPT teacher head0.210
Teacher spread0.205 · 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 designObservational
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

Citations19
Published2022
Admission routes2
Has abstractyes

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