Elevated mutation and selection in wild emmer wheat in response to 28 years of global warming
Bibliographic record
Abstract
Koern.; WEW) populations in Israel, sampling them in 1980 and again in 2008, through an exome capture analysis. It was found that these WEW populations were under elevated selection, displayed reduced diversity and temporal divergence, and carried increased mutational burdens forward. However, some populations still showed the ability to acquire beneficial alleles via selection or de novo mutation for future adaptation. Grouping populations with mean annual rainfall and temperature revealed significant differences in most of the 14 genetic estimates in either sampling year or over the 28 y. The patterns of genetic response to rainfall and temperature varied and were complex. In general, temperature groups displayed more temporal differences in genetic response than rainfall groups. The highest temperature group had more deleterious single nucleotide polymorphisms (dSNPs), higher nucleotide diversity, fewer selective sweeps, lower differentiation, and lower mutational burden. The least rainfall group had more dSNPs, higher nucleotide diversity, lower differentiation and higher mutational burden. These characterized genetic responses are significant, allowing not only for better understanding of evolutionary changes in the threatened populations, but also for realistic modeling of plant population adaptability and vulnerability to global warming.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".