Recurrent selection for physiological resistance to white mould in dry bean
Bibliographic record
Abstract
With 1 figure and 5 tables Abstract White mould (WM) [caused by Sclerotinia sclerotiorum (Lib.) de Bary] is a widespread disease of dry and green bean (Phaseolus vulgaris L.) in North America and elsewhere. Our objective was to determine the effectiveness of recurrent selection (RS) for pyramiding WM resistance from Andean, Middle American and inter‐specific breeding lines. Two double‐cross populations, Pop I = USPT‐WM‐1/CORNELL 601//USPT‐CBB‐1/92BG‐7 and Pop II = ‘Chase’/I 9365–25//ABL 15/A 195 were developed. Each double‐cross comprised 848 F1 plants for RS cycle one (C1). Twenty six WM‐resistant plants from each double‐cross were used to produce RS cycle two (C2). To measure the gain for WM resistance, 13 selected S2 families from each of C1 and C2 of each population and their four parents (C0) were tested in a randomized complete block design with three replicates in two greenhouse environments in 2008. Two separate inoculations on each plant one week apart using a cut‐stem method were made. The WM reaction was scored at 16, 23 and 33 days post inoculation (DPI) using a 1 (no disease symptoms) to 9 (severely diseased or dead plants) scale. The effects of environment were significant (P = 0.05), but replicate and population effects were not significant (P > 0.05). The RS was effective in changing the mean WM score of selected families from C0 to C1 and C2, but the gain between C1 and C2 was not significant in Pop I. The gain in WM resistance for Pop I was 12%. Selection gain in Pop II was 5%, but no significant differences in mean WM scores of C0, C1 and C2 were found. However, selected S2 families had WM scores similar to those of the best parent and selected families from Pop I. Thus, one cycle of RS applied to double‐cross populations was adequate for improving WM resistance in common bean.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".