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Record W4394758117 · doi:10.14814/phy2.16005

Physiological impacts of atmospheric pollution: Effects of environmental air pollution on exercise

2024· article· en· W4394758117 on OpenAlexaff
Michael S. Koehle

Bibliographic record

VenuePhysiological Reports · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicAir Quality and Health Impacts
Canadian institutionsCanadian Sport Centre PacificUniversity of British Columbia
Fundersnot available
KeywordsAir pollutionPollutionEnvironmental sciencePollutantOzoneDiesel exhaustEnvironmental healthMedicineDiesel fuelMeteorologyEcologyWaste managementBiologyEngineeringGeography

Abstract

fetched live from OpenAlex

In this review, we discuss some of the recent advances in our understanding of the physiology of the air pollution and exercise. The key areas covered include the effect of exercise intensity, the effects of pre-exposure to air pollution, acclimation to air pollution, and the utility of masks during exercise. Although higher intensity exercise leads to an increase in the inhaled dose of pollutants for a given distance traveled, the acute effects of (diesel exhaust) air pollution do not appear to be more pronounced. Second, exposure to air pollution outside of exercise bouts seems to have an effect on exercise response, although little research has examined this relationship. Third, humans appear to have an ability to acclimate to ground level ozone, but not other pollutants. And finally, masks may have beneficial effects on certain outcomes at low intensity exercise in pollution with significant levels of particles, but more study is required in realistic conditions.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.020
GPT teacher head0.282
Teacher spread0.262 · 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

Citations14
Published2024
Admission routes1
Has abstractyes

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