Efficacy of three greenhouse screening methods for the identification of physiological resistance to white mold in dry bean
Bibliographic record
Abstract
White mold (WM) caused by Sclerotinia sclerotiorum (Lib.) de Bary is the most devastating disease of common bean (dry and snap or garden bean) (Phaseolus vulgaris L.) in North America. The use of a reliable screening method (SM) in common bean is crucial to improve physiological resistance to WM. The objective of this study was to compare the efficacy of three SM to identify physiological resistance in dry bean genotypes with different evolutionary origins and levels of resistance. Screening methods tested were: (i) the modified straw test or cut–stem (CSM); (ii) infected bean flower (IFL); and (iii) infected oat seed (IOS). A 195, ICA Bunsi, Othello, and VCW 54 dry bean were tested with the three SM. The experimental design was a split plot in randomized complete blocks with three replications in 2007 and 2008. Two independent inoculations 1 wk apart for each SM were made. The WM reaction was scored at 16, 23, and 33 d post-inoculation (DPI) using a 1 to 9 scale. There were highly significant differences between SM and its interaction with years. The CSM and IFL were the most consistent and highly correlated (r > 0.70, P < 0.01). Interspecific breeding line VCW 54 consistently had the highest WM resistance across years, SM, and evaluation dates, followed by A 195. White mold scores increased with delayed evaluations. Thus, CSM or IFL with disease assessed 33 DPI should be used for identifying common bean genotypes with high levels of physiological resistance to WM.Key words: Common bean, growth habit, race Mesoamerica, race Nueva Granada, Phaseolus vulgaris, 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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".