Core cooling and thermal responses during whole-head, facial, and dorsal immersion in 17 °C water
Bibliographic record
Abstract
This study isolated the effects of dorsal, facial, and whole-head immersion in 17 degrees C water on peripheral vasoconstriction and the rate of body core cooling. Seven male subjects were studied in thermoneutral air (approximately 28 degrees C). On 3 separate days, they lay prone or supine on a bed with their heads inserted through the side of an adjustable immersion tank. Following 10 min of baseline measurements, the water level was raised such that the water immersed the dorsum, face, or whole head, with the immersion period lasting 60 min. During the first 30 min, the core (esophageal) cooling rate increased from dorsum (0.29 ± 0.2 degrees C h-1) to face (0.47 ± 0.1 degrees C h-1) to whole head (0.69 ± 0.2 degrees C h(-1)) (p < 0.001); cooling rates were similar during the final 30 min (mean, 0.16 ± 0.1 degrees C h(-1)). During the first 30 min, fingertip blood flow (laser Doppler flux as percent of baseline) decreased faster in whole-head immersion (114 ± 52% h(-1)) than in either facial (51 ± 47% h-1) or dorsal (41 ± 55% h(-1)) immersion (p < 0.03); rates of flow decrease were similar during minutes 30 to 60 (mean, 22.5 ± 19% h(-1)). Total head heat loss over 60 min was significantly different between whole-head (120.5 ± 13 kJ), facial (86.8 ± 17 kJ), and dorsal (46.0 ± 11 kJ) immersion (p < 0.001). The rate of core cooling, relative to head heat loss, was similar in all conditions (mean, 0.0037 ± 0.001 degree C kJ(-1)). Although the whole head elicited a higher rate of vasoconstriction, the face did not elicit more vasoconstriction than the dorsum. Rather, the progressive increase in core cooling from dorsal to facial to whole-head immersion simply correlates with increased heat loss.
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 | 0.000 | 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) | 0.001 | 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".