Born high, born fast: Does highland birth confer a pulmonary advantage for sea level endurance?
Bibliographic record
Abstract
Less than 7% of the world's population live at an altitude above 1500 m. Yet, as many as 67% of medalists in the 2020 men's and women's Olympic marathon, and 100% of medalists in the 2020 men's and women's Olympic 5000 m track race may have been born or raised above this otherwise rare threshold. As a possible explanation, research spanning nearly a quarter of a century demonstrates that indigenous highlanders exhibit pulmonary adaptations distinct from their lowland counterparts. These adaptations may then promote endurance performance. Indeed, healthy indigenous highlanders often exhibit a larger aerobic exercise capacity compared to sea-level residents who travel to high altitude. However, questions remain on whether high-altitude birth is advantageous for sea-level competitions. In this review, we ask whether being born at a high altitude generates an ergogenic advantage for endurance performance in the Summer Olympics-a venue that is generally held at sea level. In so doing, we distinguish between three groups of high-altitude residents: (i) the indigenous highlander, (ii) the highland newcomer, and (iii) the highland sojourner. Concentrating specifically on altitude-induced alterations to pulmonary physiology beginning in the perinatal period, we propose that if altitude-related maladaptations are avoided, genomic and developmental alterations accompanying highland birth may present benefits for endurance competitions at sea level.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".