Phenotypic and genomic adaptations to the extremely high elevation in plateau zokor (<i>Myospalax baileyi</i>)
Bibliographic record
Abstract
The evolutionary outcomes of high elevation adaptation have been extensively described. However, whether widely distributed high elevation endemic animals adopt uniform mechanisms during adaptation to different elevational environments remains unknown, especially with respect to extreme high elevation environments. To explore this, we analysed the phenotypic and genomic data of seven populations of plateau zokor (Myospalax baileyi) along elevations ranging from 2,700 to 4,300 m. Based on whole-genome sequencing data and demographic reconstruction of the evolutionary history, we show that two populations of plateau zokor living at elevations exceeding 3,700 m diverged from other populations nearly 10,000 years ago. Further, phenotypic comparisons reveal stress-dependent adaptation, as two populations living at elevations exceeding 3,700 m have elevated ratios of heart mass to body mass relative to other populations, and the highest population (4,300 m) displays alterations in erythrocytes. Correspondingly, genomic analysis of selective sweeps indicates that positive selection might contribute to the observed phenotypic alterations in these two extremely high elevation populations, with the adaptive cardiovascular phenotypes of both populations possibly evolving under the functional constrains of their common ancestral population. Taken together, phenotypic and genomic evidence demonstrates that heterogeneous stressors impact adaptations to extreme elevations and reveals stress-dependent and genetically constrained adaptation to hypoxia, collectively providing new insights into the high elevation adaptation.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".