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Record W4387965417 · doi:10.1113/ep091207

Shifting focus: Time to look beyond the classic physiological adaptations associated with human heat acclimation

2023· review· en· W4387965417 on OpenAlexafffund
Thomas A. Deshayes, Dèwanou Gilles Arnaud Sodabi, Marianne Dubord, Daniel Gagnon

Bibliographic record

VenueExperimental Physiology · 2023
Typereview
Languageen
FieldMedicine
TopicThermoregulation and physiological responses
Canadian institutionsUniversité de MontréalMontreal Heart Institute
FundersFonds de Recherche du Québec - SantéNatural Sciences and Engineering Research Council of Canada
KeywordsAcclimatizationFocus (optics)BiologyEcologyPhysicsOptics

Abstract

fetched live from OpenAlex

Planet Earth is warming at an unprecedented rate and our future is now assured to be shaped by the consequences of more frequent hot days and extreme heat. Humans will need to adapt both behaviorally and physiologically to thrive in a hotter climate. From a physiological perspective, countless studies have shown that human heat acclimation increases thermoeffector output (i.e., sweating and skin blood flow) and lowers cardiovascular strain (i.e., heart rate) during heat stress. However, the mechanisms mediating these adaptations remain understudied. Furthermore, several possible benefits of heat acclimation for other systems and functions involved in maintaining health and performance during heat stress remain to be elucidated. This review summarizes recent advances in human heat acclimation, with emphasis on recent studies that (1) advanced our understanding of the mechanisms mediating improved thermoeffector output and (2) investigated adaptations that go beyond those classically associated with heat acclimation. We highlight that these studies have contributed to a better understanding of the integrated physiological responses underlying human heat acclimation while leaving key unanswered questions that will need to be addressed in the future.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.860
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.002

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 head0.131
GPT teacher head0.401
Teacher spread0.270 · 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; both teacher heads agree on what is shown here.

Study designBench or experimental
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

Citations14
Published2023
Admission routes2
Has abstractyes

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