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Record W2961356688 · doi:10.1016/j.physbeh.2019.112623

The effect of thermal transience on the perception of thermal comfort

2019· article· en· W2961356688 on OpenAlexaff
Urša Ciuha, Kunihito Tobita, Adam C. McDonnell, Igor B. Mekjavić

Bibliographic record

VenuePhysiology & Behavior · 2019
Typearticle
Languageen
FieldMedicine
TopicThermoregulation and physiological responses
Canadian institutionsSimon Fraser University
FundersEuropean CommissionHorizon 2020Yamada Science Foundation
KeywordsThermal comfortAnimal scienceChemistryMaterials scienceMedicineMeteorologyBiologyPhysics

Abstract

fetched live from OpenAlex

The present study tested the hypothesis that at any given ambient temperature (Ta), thermal comfort (TC) is not only a function of the temperature per se, but is also influenced by the temperatures rate of change and direction. Twelve healthy young (age: 23 ± 3) male participants completed experimental trials where Ta increased from 15° to 40 °C (heating) and then decreased from 40 to 15 °C (cooling). In one trial (FAST), the rate of change in Ta was maintained at 1 °C.min−1, and in the other (SLOW) at 0.5 °C.min−1. During each trial participants provided ratings of TC at 3-min intervals to determine their thermal comfort zone (TCZ). In the FAST trial, participants identified TCZ at an Ta between 22 ± 4 and 30 ± 4 °C during heating and between 25 ± 3 and 33 ± 3 °C during cooling phase (p = .003), and in the SLOW trial between 21 ± 3 and 33 ± 4 °C during heating and between 23 ± 4 and 34 ± 3 °C during cooling phase (p = .012). During the heating phase TCZ was established at a lower range of Ta, compared to cooling phase. The difference between the heating and cooling phases in preferred range of Ta was more pronounced in the FAST compared to SLOW trial. TCZ is influenced not only by the prevailing temperature, but also by the direction and the rate of the change in Ta. Faster changes in Ta (1 °C.min−1) established the TCZ at a higher Ta during cooling and at a lower Ta during heating phase.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.893
Threshold uncertainty score0.876

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)0.0010.000

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 head0.019
GPT teacher head0.305
Teacher spread0.285 · 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

Citations25
Published2019
Admission routes1
Has abstractyes

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