Populations genetics and the evolution of herbicide resistance in weeds
Bibliographic record
Abstract
Numerous factors, including mutation, selection, inheritance, mating System, and gene flow are important in the evolution of herbicide resistance in weeds. Spontaneous gene mutation is believed to be the main source of genetic variation for resistance evolution in a geographic region in which resistance has not been detected previously. Despite mutation frequencies that are probably very low, the probability of occurrence of at least a single resistant mutant in a susceptible population may be high for weed species with high fecundities and large population sizes. Subsequent repeated treatments with herbicides having the same mode of action could lead to the rapid evolution of predominantly resistant populations. Rare dominantly inherited resistance mutations spread significantly more rapidly than recessive mutations in random mating populations, but at roughly the same rate in highly self-fertilizing species. Gene flow, through the movement of pollen or seed from resistant weed populations, may provide a source of resistance alleles to adjacent or nearby susceptible fields. Mathematical models indicate that the strength of selection imposed by a herbicide and the initial frequency of the resistant phenotype most strongly influence the rate of resistance evolution. The models predict that the most effective strategies to manage resistance are to reduce the intensity of selection by herbicide and to limit the migration of herbicide-resistant seed.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".