Relative fitnes of herbicide-resistant and susceptible biotypes of weeds
Bibliographic record
Abstract
In recent years, there has been a rapid increase in the number of reported cases of herbicide-resistant weed species (over 100), as well as an increase in the types of herbicides to which resistance has evolved. This paper reviews evidence for differential fitness of herbicide-resistant and susceptible biotypes. Fitness estimates are required to produce reliable population models. Fitness measures describe the potential evolutionary success of a genotype based on survival, competitive ability and ultimately reproductive success. Differences in relative fitness between resistant and susceptible biotypes are usually inferred from measures of relative plant productivity or competitiveness. For triazine-resistant weed species, studies have indicated that resistant plants were generally less fit than susceptible plants, although exceptions did exist. Although less data are available on the fitness of plants resistant to non-triazine herbicides, information is summarized for sulfonylureas, substituted ureas, dinitroanilines, paraquat, diclofop, and organic arsenicals. No consistent differences in relative fitness were observed for non-triazine resistant and susceptible biotypes. In general, studies have indicated that the relative fitness of susceptible and resistant biotypes of a single species depends upon biological conditions, including genotype and population variation, intra- and inter-biotype competition, and environmental conditions such as temperature, light quality, and management practices. Future needs for relative fitness studies are discussed.
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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 | 0.001 |
| 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".