Selecting the correct exercise intensity for unbiased comparisons of thermoregulatory responses between groups of different mass and surface area
Bibliographic record
Abstract
We assessed whether comparisons of thermoregulatory responses between groups unmatched for body mass and surface area (BSA) should be performed using a metabolic heat production (prod) in Watts or Watts per kilogram for changes in rectal temperature (ΔTre), and an evaporative heat balance requirement (Ereq) in Watts or Watts per square meter for local sweat rates (LSR). Two groups with vastly different mass and BSA [large (LG): 91.5 ± 6.8 kg, 2.12 ± 0.09 m(2), n = 8; small (SM): 67.6 ± 5.6 kg, 1.80 ± 0.09 m(2), n = 8; P < 0.001], but matched for heat acclimation status, sex, age, and with the same onset threshold esophageal temperatures (LG: +0.37 ± 0.12°C; SM: +0.41 ± 0.17°C; P = 0.364) and thermosensitivities (LG: 1.02 ± 0.54, SM: 1.00 ± 0.38 mg·cm(-2)·min(-1)·°C(-1); P = 0.918) for sweating, cycled for 60 min in 25°C at different levels of prod (500 W, 600 W, 6.5 W/kg, 9.0 W/kg) and Ereq (340 W, 400 W, 165 W/m(2), 190 W/m(2)). ΔTre was different between groups at a prod of 500 W (LG: 0.52 ± 0.15°C, SM: 0.92 ± 0.24°C; P < 0.001) and 600 W (LG: 0.78 ± 0.19°C, SM: 1.14 ± 0.24°C; P = 0.007), but similar at 6.5 W/kg (LG: 0.79 ± 0.21°C, SM: 0.85 ± 0.14°C; P = 0.433) and 9.0 W/kg (LG: 1.02 ± 0.22°C, SM: 1.14 ± 0.24°C; P = 0.303). Furthermore, ΔTre was the same at 9.0 W/kg in a 35°C environment (LG: 1.12 ± 0.30°C, SM: 1.14 ± 0.25°C) as at 25°C (P > 0.230). End-exercise LSR was different at Ereq of 400 W (LG: 0.41 ± 0.18, SM: 0.57 ± 0.13 mg·cm(-2)·min(-1); P = 0.043) with a trend toward higher LSR in SM at 340 W (LG: 0.28 ± 0.06, SM: 0.37 ± 0.15 mg·cm(-2)·min(-1); P = 0.057), but similar at 165 W/m(2) (LG: 0.28 ± 0.06, SM: 0.28 ± 0.12 mg·cm(-2)·min(-1); P = 0.988) and 190 W/m(2) (LG: 0.41 ± 0.18, SM: 0.37 ± 0.15 mg·cm(-2)·min(-1); P = 0.902). In conclusion, when comparing groups unmatched for mass and BSA, future experiments can avoid systematic differences in ΔTre and LSR by using a fixed prod in Watts per kilogram and Ereq in Watts per square meter, respectively.
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 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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".