Changing patterns of genetic differentiation in the slender wild oat, <i>Avena barbata</i>
Bibliographic record
Abstract
The slender wild oat ( Avena barbata ) was widely studied in California using allozymes in the 1970s and interpreted as a case of ecotypic adaptation to contrasting moisture environments. However, common garden studies suggested that the moist-associated (“mesic”) ecotype had high fitness in both moist and dry habitats, thus predicting an adaptive spread into areas occupied by the dry associated (“xeric”) ecotype. To test this prediction, we revisited 100 populations of A. barbata that were screened genetically 40 y ago. As expected, mesic allozyme and morphological markers are much more common than in the 1970s. The less-fit xeric ecotype, while still widespread, has declined markedly in range and frequency. Genotyping by sequencing of modern populations reveals striking genetic uniformity within each of the two ecotypes. In recombinants between the two ecotypes, the mesic allele at a major fitness quantitative trait locus (QTL) shows a high frequency but so do many other genomic regions not identified as fitness QTL. Additional introduced genotypes are diverse and more widespread than in the past, and our results show that these have spread into the former range of the xeric ecotype to an even greater extent than the mesic ecotype has. While these results confirm the prediction of contemporary evolution from common gardens, they also suggest that much of the change has been driven by additional waves of introduced genotypes.
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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 | 0.000 |
| 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".