Gamete and recurrent selection for improving physiological resistance to white mold in common bean
Bibliographic record
Abstract
White mold (WM) is a widely distributed and severe disease of common bean in North America. The objective of this study was to determine the effectiveness of gamete (GS) and recurrent (RS) selection methods for improving WM resistance. Two double-crosses, namely USPT-WM-1/CORNELL 601//USPT-CBB-1/92BG-7 and ‘Chas’/I 9365-25//ABL 15/A 195 were made. Equal numbers of progenies from both crosses were subjected to GS and RS. The GS was practiced from F1 to F4 by selecting WM resistant single plants followed by progeny testing in the subsequent generation. Two cycles of RS were practiced by intermating selected WM resistant plants in each cycle. Thirteen selected families in each method and parents were evaluated at 16, 23, and 33 days post first inoculation in replicated trials in two greenhouse environments. Higher frequencies of families with lower WM scores were obtained from GS than RS. The average genetic gains due to GS and RS were 19.6 and 7.9%, respectively. Employing multiple-parent-crosses involving parents of diverse evolutionary origins delayed WM evaluation, and application of GS are recommended for improving physiological WM resistance in common bean.Key words: Interspecific breeding line, introgressing resistance, multiple-parent crosses, Phaseolus vulgaris, pyramiding resistance, Sclerotinia sclerotiorum
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".