Bibliographic record
Abstract
Reviews on whole body human cold adaptation generally do not distinguish between population studies and dedicated acclimation studies, leading to confusing results. Population studies show that indigenous black Africans have reduced shivering thermogenesis in the cold and poor cold induced vasodilation in fingers and toes compared to Caucasians and Inuit. About 40,000 y after humans left Africa, natives in cold terrestrial areas seems to have developed not only behavioral adaptations, but also physiological adaptations to cold. Dedicated studies show that repeated whole body exposure of individual volunteers, mainly Caucasians, to severe cold results in reduced cold sensation but no major physiological changes. Repeated cold water immersion seems to slightly reduce metabolic heat production, while repeated exposure to milder cold conditions shows some increase in metabolic heat production, in particular non-shivering thermogenesis. In conclusion, human cold adaptation in the form of increased metabolism and insulation seems to have occurred during recent evolution in populations, but cannot be developed during a lifetime in cold conditions as encountered in temperate and arctic regions. Therefore, we mainly depend on our behavioral skills to live in and survive the cold.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".