Characterisation of target‐site resistance to ACCase‐inhibiting herbicides in the weed <i>Alopecurus myosuroides</i> (black‐grass)
Bibliographic record
Abstract
Resistance to aryloxyphenoxypropionate (AOPP), cyclohexanedione (CHD) and phenylurea herbicides was determined in UK populations of Alopecurus myosuroides Huds. Two populations (Oxford AA1, Notts. A1) were highly resistant (Resistance indices 13-->1000) to the AOPP and CHD herbicides fenoxaprop, diclofop, fluazifop-P and sethoxydim, but only marginally resistant to the phenylurea, chlorotoluron. Analyses of acetyl coenzyme A carboxylase (ACCase) activity showed that an insensitive ACCase conferred resistance to all the AOPP/CHD herbicides investigated. Another population, Oxford S1, showed no resistance to sethoxydim at the population level, but contained a small proportion of plants (<10%) with an insensitive ACCase. Genetic studies on the Notts A1 and Oxford S1 populations demonstrated that target site resistance conferred by an insensitive ACCase is monogenic, nuclearly inherited with the resistant allele showing complete dominance. Investigations of the molecular basis of resistance in the Notts A1 population showed that sethoxydim resistance in A myosuroides was associated with the substitution of an isoleucine in susceptible with a leucine in resistant plants, which has also been found in three other resistant grass-weed species (Setaria viridis (L) Beauv, Avena fatua L, Lolium rigidum Gaud).
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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 | 0.000 | 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".