Early detection and management of herbicide-resistant weeds
Bibliographic record
Abstract
Small weed patches may be noticed in fields after herbicide application, but they typically do not have a significant impact on the season’s crop yield. As a result, they are usually not treated as a threat to future yields. However, if these patches harbour weed biotypes resistant to one or multiple herbicides, resistance alleles can spread both spatially (via various dispersal pathways, including seed transport by machinery and commodity contamination) and temporally (through seed persistence). This poses a significant threat to herbicide-based weed management. Once these populations spread and cover a large enough area, eradication becomes improbable despite all the resistance management efforts. Therefore, a proactive and collaborative endeavour is needed to detect and manage small and patchy resistant weed populations. In this paper, we review the current potential of weed resistance detection using imagery and molecular markers as well as possible weed management approaches. Finally, we advocate for the use of a combination of these techniques to manage herbicide-resistant weeds when populations are small. This multifaceted approach is presently not applicable to all resistance mechanisms, and all weed species located in any crop, but could initially focus on biotypes and species that are easy to detect and represent the greatest threat.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".