A three-compartment thermometry model for the improved estimation of changes in body heat content
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".