Genetic approaches to understanding human adaptation to altitude in the Andes
Bibliographic record
Abstract
Despite the initial discomfort often experienced by visitors to high altitude, humans have occupied the Andean altiplano for more than 10000 years, and millions of people, indigenous and otherwise, currently live on these plains, high in the mountains of South America, at altitudes exceeding 3000 m. While, to some extent, acclimatization can accommodate the one-third decrease in oxygen availability, having been born and raised at altitude appears to confer a substantial advantage in high-altitude performance compared with having been born and raised at sea level. A number of characteristics have been postulated to contribute to a high-altitude Andean phenotype; however, the relative contributions of developmental adaptation (within the individual) and genetic adaptation (within the population of which the individual is part) to the acquisition of this phenotype have yet to be resolved. A complex trait is influenced by multiple genetic and environmental factors and, in humans, it is inherently very difficult to determine what proportion of the trait is dictated by an individual's genetic heritage and what proportion develops in response to the environment in which the person is born and raised. Looking for changes in putative adaptations in vertically migrant populations, determining the heritability of putative adaptive traits and genetic association analyses have all been used to evaluate the relative contributions of nurture and nature to the Andean phenotype. As the evidence for a genetic contribution to high-altitude adaptation in humans has been the subject of several recent reviews, this article instead focuses on the methodology that has been employed to isolate the effects of 'nature' from those of 'nurture' on the acquisition of the high-altitude phenotype in Andean natives (Quechua and Aymara). The principles and assumptions underlying the various approaches, as well as some of the inherent strengths and weaknesses of each, are briefly discussed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".