Finger anthropometrics may not be a primary influence on the thermal responses to cooling and rewarming
Bibliographic record
Abstract
The fingers have a large surface area to volume ratio (SA:V), minimal muscle mass, and potent vasoconstrictor capacity. These qualities make the fingers prone to heat loss and freezing injuries during whole-body or local cold exposure. Anthropologists have proposed that the large inter-individual variability in human finger anthropometrics may be an ecogeographic evolutionary adaptation, where shorter and thicker digits (i.e. smaller SA:V ratio) provide a favorable adaptation for cold climate natives. We hypothesized that the SA:V ratio of a digit has an inverse relationship with finger blood flux and finger temperature (Tfinger) during cooling and rewarming from cold. Fifteen healthy adults with no or limited cold experiment experience performed 10 min of baseline immersion in warm water (35.0 ± 0.1°C), 30 min in cold water (8.4 ± 0.2°C), and a final 10 min of rewarming in ambient air (~22°C, ~40% relative humidity). Tfinger and finger blood flux were measured continuously across multiple digits per participant. Average Tfinger (p = 0.05; R2 = 0.06) and area under the curve for Tfinger (p = 0.05; R2 = 0.07) during hand cooling showed significant, negative correlations to digit SA:V ratio. There was no relationship between digit SA:V ratio and blood flux (i.e. average blood flux and AUC) during cooling as well as between SA:V ratio and digit temperature (i.e. average Tfinger and AUC) or blood flux (i.e. average blood flux and AUC) during rewarming. Overall, digit anthropometrics do not appear to play a dominant role in extremity cold response.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".