Direct and Marker‐Assisted Selection for Resistance to Common Bacterial Blight in Common Bean
Bibliographic record
Abstract
ABSTRACT Common bacterial blight (CBB) caused by Xanthomonas campestris pv. phaseoli and X. campestris pv. phaseoli var. fuscans is a seed‐borne disease that can significantly reduce yield and seed quality of common bean (Phaseolus vulgaris L.). Disease resistance is the most effective management strategy, but most common bean cultivars have no CBB resistance. Marker‐assisted selection (MAS) shows promise as a method to facilitate pyramiding CBB resistance between common bean gene pools. This study compared the efficiency of direct disease resistance selection (DDS) and MAS for the pyramiding and transfer of CBB resistance into dark red kidney bean. Direct disease resistance selection and MAS were applied independently, from the F1 through the F5, in an inter‐gene pool double‐cross population, Wilkinson 2/dark red kidney (DRK) 2//DRK 1/VAX 3. Replicated trials were performed in the greenhouse and field in 2006 and 2007 to assess levels of CBB resistance in 15 breeding lines independently developed with each method. Under high disease pressure, in two greenhouse environments, 12 resistant breeding lines were obtained with DDS compared with six using MAS, and the overall CBB mean disease severity index was 3.3 for DDS and 4.2 for MAS. Under moderate disease pressure in the field, there was no significant difference (p > 0.05) between the responses of breeding lines developed by the two selection methods. The cost of DDS was US$1.55 per plant compared to $2.03 for MAS.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".