Shivering Heat Production and Core Cooling During Head-In and Head-Out Immersion in 17°C Water
Bibliographic record
Abstract
INTRODUCTION: Many cold-water scenarios cause the head to be partially or fully immersed (e.g., ship wreck survival, scuba diving, cold-water adventure swim racing, cold-water drowning, etc.). However, the specific effects of head cold exposure are minimally understood. This study isolated the effect of whole-head submersion in cold water on surface heat loss and body core cooling when the protective shivering mechanism was intact. METHODS: Eight healthy men were studied in 17 degrees C water under four conditions: the body was either insulated or exposed, with the head either out of the water or completely submersed under the water within each insulated/exposed subcondition. RESULTS: Submersion of the head (7% of the body surface area) in the body-exposed condition increased total heat loss by 11% (P < 0.05). After 45 min, head-submersion increased core cooling by 343% in the body-insulated subcondition (head-out: 0.13 +/- 0.2 degree C, head-in: 0.47 +/- 0.3 degree C; P < 0.05) and by 56% in the body-exposed subcondition (head-out: 0.40 +/- 0.3 degree C and head-in: 0.73 +/- 0.6 degree C; P < 0.05). DISCUSSION: In both body-exposed and body-insulated subconditions, head submersion increased the rate of core cooling disproportionally more than the relative increase in total heat loss. This exaggerated core-cooling effect is consistent with a head cooling induced reduction of the thermal core, which could be stimulated by cooling of thermosensitive and/or trigeminal receptors in the scalp, neck, and face. These cooling effects of head submersion are not prevented by shivering heat production.
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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) | 0.002 | 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".