High gene flow maintains genetic diversity following selection for high EPSPS copy number in the weed kochia (Amaranthaceae)
Bibliographic record
Abstract
Abstract Kochia, a major weed species, has evolved resistance to four herbicide modes of action. Herbicide resistance appears to spread quickly, which could result in diminished standing genetic variation, reducing the ability of populations to adapt further. Here we used double digest restriction enzyme associated sequencing to determine the level of gene flow among kochia populations and whether selection for glyphosate resistance reduces genetic variation. Canadian Prairie populations show little to no genetic differentiation (FST = 0.01) and no correlation between genetic and geographic distance (r2 = − 0.02 p = 0.56), indicating high gene flow and no population structure. There is some evidence that kochia populations are genetically depauperate compared to other weed species, but genetic diversity did not differ between glyphosate susceptible and resistant populations or individuals. The inbreeding coefficients suggest there are 23% fewer heterozygotes in these populations than expected with random mating, and no variation was found within the chloroplast. These results indicate that any alleles for herbicide resistance can be expected to spread quickly through kochia populations, but there is little evidence this spread will reduce the species’ genetic variation or limit the species’ ability to respond to further selection pressure.
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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".