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Record W2172167163 · doi:10.1152/ajpregu.00338.2006

A three-compartment thermometry model for the improved estimation of changes in body heat content

2006· article· en· W2172167163 on OpenAlexaff
Ollie Jay, Louise M. Gariepy, Francis D. Reardon, Paul W. Webb, Michel B. Ducharme, Tim Ramsay, Glen P. Kenny

Bibliographic record

VenueAmerican Journal of Physiology-Regulatory, Integrative and Comparative Physiology · 2006
Typearticle
Languageen
FieldMedicine
TopicThermoregulation and physiological responses
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsCompartment (ship)Content (measure theory)Biological systemMathematicsBiologyGeology

Abstract

fetched live from OpenAlex

The aim of this study was to use whole body calorimetry to directly measure the change in body heat content (DeltaH(b)) during steady-state exercise and compare these values with those estimated using thermometry. The thermometry models tested were the traditional two-compartment model of "core" and "shell" temperatures, and a three-compartment model of "core," "muscle," and "shell" temperatures; with individual compartments within each model weighted for their relative influence upon DeltaH(b) by coefficients subject to a nonnegative and a sum-to-one constraint. Fifty-two participants performed 90 min of moderate-intensity exercise (40% of Vo(2 peak)) on a cycle ergometer in the Snellen air calorimeter, at regulated air temperatures of 24 degrees C or 30 degrees C and a relative humidity of either 30% or 60%. The "core" compartment was represented by temperatures measured in the esophagus (T(es)), rectum (T(re)), and aural canal (T(au)), while the "muscle" compartment was represented by regional muscle temperature measured in the vastus lateralis (T(vl)), triceps brachii (T(tb)), and upper trapezius (T(ut)). The "shell" compartment was represented by the weighted mean of 12 skin temperatures (T(sk)). The whole body calorimetry data were used to derive optimally fitting two- and three-compartment thermometry models. The traditional two-compartment model was found to be statistically biased, systematically underestimating DeltaH(b) by 15.5% (SD 31.3) at 24 degrees C and by 35.5% (SD 21.9) at 30 degrees C. The three-compartment model showed no such bias, yielding a more precise estimate of DeltaH(b) as evidenced by a mean estimation error of 1.1% (SD 29.5) at 24 degrees C and 5.4% (SD 30.0) at 30 degrees C with an adjusted R(2) of 0.48 and 0.51, respectively. It is concluded that a major source of error in the estimation of DeltaH(b) using the traditional two-compartment thermometry model is the lack of an expression independently representing the heat storage in muscle during exercise.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.016
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
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 head0.060
GPT teacher head0.327
Teacher spread0.267 · 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 designSimulation or modeling
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

Citations105
Published2006
Admission routes1
Has abstractyes

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