Detection of resistance to acetolactate synthase inhibitors in weeds with emphasis on DNA‐based techniques: a review
Bibliographic record
Abstract
Resistance to herbicides inhibiting acetolactate synthase (ALS) has been increasing at a faster rate than in any other herbicide group. The great majority of these cases are due to various single-nucleotide polymorphisms in the ALS gene endowing target site resistance. Many diagnostic techniques have been devised in order to confirm resistance and help producers to adopt the best management strategies. Recent advances in DNA technologies coupled with the knowledge of sequence information have allowed the development of accurate and rapid diagnostic tests. While whole plant-based diagnostic techniques such as seedling bioassays or enzyme-based in vitro bioassays provide accurate results, they tend to be labour- and/or space-intensive and will only respond to the particular herbicides tested, making resolution of cross-resistance patterns more difficult. Successful DNA-based diagnosis of ALS inhibitor resistance has been achieved with three main techniques, (1) restriction fragment length polymorphism, (2) polymerase chain reaction amplification of specific alleles and (3) denaturing high-performance liquid chromatography. All DNA-based techniques are relatively rapid and provide clear identification of the mutations causing resistance. Resistance based on non-target mechanisms is not identified by these DNA-based methods; however, given the prevalence of target site-based ALS inhibitor resistance, this is a minor inconvenience.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".