Integrated weed management strategies for delaying herbicide resistance in wild oats
Bibliographic record
Abstract
Herbicide-resistant biotypes of wild oats (Avena fatua) infest most major cereal producing regions in the western United States and Canada. This paper reviews potential integrated weed management strategies that can be used to prevent or delay selection of herbicide-resistant wild oats plants. An integrated wild oats management strategy to delay or prevent the development of herbicide resistance should be based on preventing the movement of wild oats seed into the soil. Two ways to achieve this are by preventing the immigration of seed into the field from external sources, and by reducing or eliminating seed production by wild oats already in the field. It is becoming increasingly clear that reliance on continuous herbicide useas the sole means of weed control will fail to eliminate wild oats and other weed seed from the soil seedbank. On the contrary, evidence is mounting that this practice will select for biotypes that are resistant to the herbicides used, especially where herbicides of the same mode of action are used continuously. It is essential, therefore, that herbicides be considered as just one component of an overall integrated System together with cultural control and other management strategies, and that agronomic principles be considered when developing this System.
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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 | 0.001 | 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".