Determining cold strain in cold air: a comparison of two methods of partitional calorimetry to calculate heat storage and debt in cold air with mild hypothermia
Bibliographic record
Abstract
We compared two methods of partitional calorimetry to calculate heat storage and heat debt during cold air (0°C) exposure causing mild core cooling. Twelve participants performed a 5 min baseline in thermoneutral conditions (∼22.0°C, ∼50% relative humidity) followed by cold air exposure (∼0°C) until rectal temperature was reduced by ∆−0.5°C. Partitional calorimetry was used to calculate avenues of heat exchange (radiative, convective, and evaporative), heat storage, and heat debt continuously throughout cold exposure. We compared deriving these variables using prediction equations based on environmental and participant characteristics (PCALEquation Method) versus using measurement tools such as humidity sensors and heat flux discs (PCALHeat Flux Method). There were significant differences between methods (all p ≤ 0.001) for determining heat exchange, heat storage, and heat debt. At ∆−0.5°C, PCALHeat Flux Method had greater levels of radiative and convective heat exchange (PCALHeat Flux Method: −143.0 ± 16.8 W∙m2 vs PCALEquation Method: −123.0 ± 12.9 W∙m2, p ≤ 0.001), evaporative heat exchange (PCALHeat Flux Method: −9.0 ± 1.7 W∙m2 vs PCALEquation Method: −4.1 ± 0.0 W∙m2, p ≤ 0.001), heat storage (PCALHeat Flux Method: −15.0 ± 31.0 W∙m2 vs PCALEquation Method: +6.0 ± 25.9 W∙m2, p = 0.020), and heat debt (PCALHeat Flux Method: −692.0 ± 315.0 kJ vs PCALEquation Method: −422.0 ± 136.0 kJ, p ≤ 0.001). Overall, this study found the largest discrepancies between the two methods were when the environmental conditions and skin temperature were in high flux, as well as when core temperature was reduced by ∆−0.5°C. The use of PCALHeat Flux Method may be more advantageous to use in the cold to provide a higher resolution measurement of cold strain.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.001 | 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".