Herbicide resistance in <i>Alopecurus myosuroides</i> : The value of routine testing of seed samples submitted by farmers since 1985
Bibliographic record
Abstract
Abstract In 35 years of routine screening for herbicide resistance, a total of 3758 seed sample/herbicide combinations were assayed. Resistance testing was done in either pots in glasshouses (chlortoluron, fenoxaprop and mesosulfuron‐methyl + iodosulfuron‐methyl sodium) or Petri‐dishes in incubators (sethoxydim, cycloxydim and pendimethalin). With all herbicides, the relationship between herbicide efficacy and year of sampling was linear, with the slope representing the annual loss of efficacy. This was higher for the ALS inhibitors mesosulfuron + iodosulfuron (3.73% year −1 ) and ACCase inhibitors sethoxydim/cycloxydim (1.92% year −1 ) and fenoxaprop (1.36% year −1 ) than for the substituted urea chlorotoluron (0.69% year −1 ) and the dinitroaniline pendimethalin (1.10% year −1 ). These results are consistent with other studies on the relative resistance risk associated with these different modes of action. There was also a surprisingly good correlation between results for random and non‐random resistance testing, which has also been found in studies with other weed species in Canada and Australia. This indicates that routine testing of submitted samples can replace, at least partly, the need for random surveys which tend to be both labour intensive and expensive. These results, compiled over 35 years, show the value of routine resistance screening, not only for detecting resistance at the individual field level (‘micro’ scale), but also the distribution, evolution and impact of resistance country‐wide (‘macro’ scale). However, it is important that standardised testing methods, including appropriate reference populations, are used by different testing centres to ensure consistent results.
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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) | 0.000 | 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".