Genetic Analysis of Imidazolinone Resistance in Mutation‐Derived Lines of Common Wheat
Bibliographic record
Abstract
The imidazolinone herbicides possess high biological potency at low application rates, and thus are an attractive alternative for weed control. The induction of genes conferring resistance by mutagenesis could facilitate the use of imidazolinones as an alterative weed control system in spring wheat (Triticum aestivum L.). Six M3:6 spring wheat lines resistant to imidazolinone herbicides were identified following seed mutagenesis and were selected for genetic study. The lines were designated as 1A, 9A, 10A, 11A, 15A, and 16A. BW755 carries a previously characterized partially dominant resistance gene (FS‐4). On the basis of analysis of F1, F2, backcross (BC)1F1 and F2:3 populations, resistance in lines 1A, 9A, 10A, 11A, and 16A is a partially dominant trait inherited as a single nuclear gene. Resistance in TealIMI 15A is dominant and is inherited as two independent nuclear‐coded genes. Allelism studies indicated that resistance genes in 1A, 9A, 10A, 16A, and one of the resistance genes in 15A are allelic to FS‐4 All crosses between resistant lines and 11A produced segregating F2 and F2:3 populations suggesting the presence of a unique resistance gene in 11A. The resistance genes were named on the basis of the recommended rules for gene symbolization in wheat. The FS‐4 allele was redesignated as Imi1 The resistance gene in 11A and the second resistance gene in 15A were designated as Imi2 and Imi3, respectively. Results from these studies indicate that higher levels of imidazolinone resistance in wheat could be achieved by stacking two or more genes into a single genotype.
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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) | 0.001 | 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 | 0.001 |
| 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".