’ome on the Range: Altitude Adaptation, Positive Selection, and Himalayan Genomics
Bibliographic record
Abstract
In 2010, a number of papers were published describing data from genome-wide studies designed to identify genes and genetic variants that contribute (or contributed) to human adaptation to altitude in the Himalaya. The results were exciting, intriguing, and controversial. Several genes, most notably EGLN1 and EPAS1, were identified as strong candidates for a role in evolutionary adaptation to high altitude, and the time course over which this adaptation occurred was calculated by one team to be remarkably brief. Overall, the data suggest that, at least in the ancestors of the modern Tibetans, there was a powerful selective pressure favoring variants in genes central to the molecular response to hypoxia. The most obvious manifestation of this selection seems to be the Tibetan's well known blunted erythropoietic response to hypoxemia. This article briefly reviews recent developments in 'omic' analysis of Tibetan highland natives, with a focus both on the answers found and the questions raised.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".