Development of near‐isogenic lines and identification of markers linked to auxinic herbicide resistance in wild mustard ( <b> <i>Sinapis arvensis</i> </b> L.)
Bibliographic record
Abstract
BACKGROUND: Auxinic herbicides are widely used for selective control of many broadleaf weeds, e.g. wild mustard. An auxinic-herbicide-resistant wild mustard biotype may offer an excellent model system to elucidate the mechanism of action of these herbicides. Classical genetic analyses demonstrate that the wild mustard auxinic herbicide resistance is determined by a single dominant gene. Availability of near-isogenic lines (NILs) of wild mustard with auxinic herbicide resistance (R) and herbicide susceptibility (S) will help to study the fitness penalty as well as the precise characterization of this gene. RESULTS: Eight generations of backcrosses were performed, and homozygous auxinic-herbicide-resistant and auxinic-herbicide-susceptible NILs were identified from BC(8) F(3) families. S plants produced significantly more biomass and seed compared with R plants, suggesting that wild mustard auxinic herbicide resistance may result in fitness reduction. It was also found that the serrated margin of the first true leaf was closely linked to auxinic herbicide resistance. Using the introgressed progeny, molecular markers linked to auxinic herbicide resistance were identified, and a genetic map was constructed. CONCLUSION: The fitness penalty associated with the auxinic herbicide resistance gene may explain the relatively slow occurrence and spread of auxinic-herbicide-resistant weeds. The detection of the closely linked markers should hasten the identification and characterization of this gene.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".