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Record W1966188631 · doi:10.1139/h04-024

The Effects of a Moderate Physical Activity Program on Thermoregulatory Responses in a Warm Environment in Men

2004· article· en· W1966188631 on OpenAlexaffabout
Casie Lee Shields, Gordon G. Giesbrecht, Grant N. Pierce, A. Elizabeth Ready

Bibliographic record

VenueCanadian Journal of Applied Physiology · 2004
Typearticle
Languageen
FieldMedicine
TopicThermoregulation and physiological responses
Canadian institutionsResearch Manitoba
Fundersnot available
KeywordsEnergy expenditureThermoregulationPhysical therapyCore temperatureMedicinePhysical fitnessHealth benefitsRecreationPsychologyExercise physiologyGerontologyInternal medicineBiologyEcology

Abstract

fetched live from OpenAlex

Moderate intensity training induces health benefits, but its influence on thermoregulatory responses during exercise in a warm environment is unclear. Twelve inactive men (mean age 24.0 +/- 6.5 yrs) underwent exercise heat tests, and peak VO2 tests, before and after a moderate training program (n= 8) or no training intervention (n = 4). Assignment to groups was random. All subjects were initially below the guidelines for physical activity set forth by Health Canada, the Canadian Society for Exercise Physiology (CSEP), and the U.S. Surgeon General. Those in the 12-week training program participated in activities such as cycling, walking, and recreational sports. Subjects were instructed to train at 50% VO2 reserve, and to gradually increase energy expenditure beyond the above mentioned recommendations. In the training group, peak VO2 increased 13%, p < 0.01, and resting peripheral blood flow during heat stress increased, p < 0.01, indicating some initial thermoregulatory benefits. No significant differences were observed in esophageal temperature or sweating threshold. Health benefits associated with thermoregulatory adaptations to exercise in a warm environment may require more vigorous exercise than recommended by current physical activity guidelines.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.937
Threshold uncertainty score0.345

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 head0.013
GPT teacher head0.262
Teacher spread0.249 · 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 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

Citations4
Published2004
Admission routes2
Has abstractyes

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