The influence of digit size and proportions on dexterity during cold exposure
Bibliographic record
Abstract
OBJECTIVES: The current study investigated whether size and proportions of the hands and digits affect dexterity during severe cold exposure. As wide hands are known to lose less heat than narrow hands, and narrow digits are associated with greater dexterity, this study aimed to test whether there was a direct trade-off between dexterity and thermoregulation that shapes hand morphology. METHODS: Participants (25 women, 15 men) carried out the Purdue Pegboard test before and after a 3-min ice-water immersion of the hand. Their hand length, hand width, digit lengths, and digit widths were measured using standard anthropometric methods. RESULTS: Wide first and third digits associated with significantly reduced dexterity after immersion relative to individuals with narrower first and third digits. Second digit width positively correlated with average digit temperature after immersion. Hand length and hand width did not influence dexterity. CONCLUSION: The current study suggests that digit width influences dexterity in cold conditions, reflecting patterns found at room temperature. Hand and digit morphology may be the product of two significant constraints on the hand: dexterity and thermoregulation. In cold conditions, hand morphology appears to be predominantly constrained by thermal stress, at the expense of dexterity. This may have important implications for interpreting the morphology of extinct and extant hominins.
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 | 0.002 |
| 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".