DNA-based diagnostic tests for the detection of acetolactate synthase-inhibiting herbicide resistance in Amaranthus sp.
Bibliographic record
Abstract
ALS-inhibitor resistance is increasing in Ontario, and at least fifty resistant populations of 'Amaranthus powellii' and ' A. retroflexus' have been confirmed as of April 2004. Resistance in these populations is due to the presence of one of four single nucleotide substitutions that alter different amino acids in the 'als' gene (A122T, A205V, W574L, S653T). Herbicide resistance can be detected by a seedling bioassay but this method takes too much time. PCR-RFLP and PASA are two DNA-based tests that rely on PCR. They have been used to detect herbicide resistance-conferring single nucleotide substitutions in other species and are much faster than a seedling bioassay. PCR Restriction Fragment Length Polymorphism (PCR-RFLP) relies on PCR amplification of ' als' followed by digestion with specific restriction endonucleases. Each resistance-conferring substitution results in the gain or loss of a recognition site and thus fragments from resistant and wildtype PCR products differ in size. PCR Amplification of Specific Alleles (PASA) uses an allele-specific primer with a 3' terminal mismatch to wildtype DNA. (Abstract shortened by UMI.)
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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) | 0.004 | 0.002 |
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".