Large differences in body fat do not independently alter changes in core temperature during exercise (1104.2)
Bibliographic record
Abstract
No previous study has isolated the independent influence of body fat (BF) on thermoregulatory responses from the biophysical factors of body mass and metabolic heat production (H prod ). Therefore, six lean (L, BF:11.1±4.2%) and six non‐lean (NL, BF:32.1±7.1%) males matched for total body mass (TBM, L: 89.6 ±7.9 kg, NL: 91.0±7.0 kg; P= 0.74), cycled for 60 min in a 28.2 ±0.2°C and 30 ±9% RH room at i) a H prod of 490 W; and ii) a H prod of 7.5 W/kg lean body mass (LBM). Rectal (T re ) and esophageal (T es ) temperatures, and local sweat rate (LSR) were measured continuously; while whole body sweat loss (WBSL) was measured from 0‐60 mins. At 490 W, changes in T re (L: 0.75 ±0.16°C, NL: 0.78 ±0.15°C), T es (L: 0.49 ±0.15°C, NL: 0.51 ±0.14°C), MLSR (L: 0.61±0.18, NL: 0.51±0.12 mgcm ‐2 min ‐1 ) and WBSL (L: 568±85 mL, NL: 565±80 mL) were similar (P>0.80). At 7.5 W/kg LBM, the L group had greater changes in T re (L: 0.92 ±0.18°C, NL: 0.53 ±0.12°C), T es (L: 0.62 ±0.17°C, NL: 0.41 ±0.12°C), MLSR (L: 0.72±0.31, NL: 0.40±0.12 mgcm ‐2 min ‐1 ) and WBSL (L: 556±104 mL, NL: 409±94 mL) (P<0.05). In conclusion, i) body fat does not independently alter thermoregulatory responses during exercise; ii) core temperature comparisons between groups differing in BF should be performed using a H prod normalized for TBM, not LBM. Grant Funding Source : Supported by, NSERC Discovery Grant, Grant holder: Ollie Jay
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".