Body heat storage during dynamic exercise – A Comparison of direct calorimetry and thermometry
Bibliographic record
Abstract
Heat storage is commonly estimated from body temperature derived from a weighted sum of mean skin and core temperatures, body mass, and the body specific heat value of 3.47 kJ·kg −1 . This model assumes uniformity of heat distribution throughout the body space and similar relative mass and composition of the body. We evaluated whether there are differences in the heat storage calculated by direct calorimetry compared to the heat storage obtained by thermometry measured during exercise. 41 subjects exercised in a Snellen air calorimeter on a cycle ergometer at 40% of VO 2peak until steady state rectal temperature was achieved. Oxygen consumption, sensible and insensible heat loss and core, muscle and skin temperatures were measured continuously. Body heat content (ΔH b ) was calculated as follows: ΔH b = Δ((0.80·T re )+(0.20·T sk )) · body mass · C p (Burton,, Nutr 9:261, 1935). Specific heat capacity (C p ) for each subject was calculated by partitioning body weight into fat, lean and bone by dual energy x‐ray absorptiometry. Changes in H b were 203 ± 78 W and 148 ± 58 W as measured by calorimetry and thermometry respectively (P<0.05). We proposed a new model incorporating muscle tissue temperature to estimate ΔH b which significantly improved the estimate of ΔH b as compared to conventional thermometry (212 ± 55 W vs. 148 ± 58 W, P<0.05). In summary, changes in ΔH b measured by thermometry are significantly underestimated when compared to those values measured by direct calorimetry. Funded by U.S. Army Medical Research Acquisition Activity and Natural Sciences and Engineering Research Council of Canada.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".