Interaction of common bacterial blight quantitative trait loci in a resistant inter‐cross population of common bean
Bibliographic record
Abstract
Abstract Common bacterial blight (CBB), caused by Xanthomonas axonopodis pv. phaseoli, is an important disease of common bean (Phaseolus vulgaris L.). Genetic resistance is the most economically efficient, environmentally friendly and socially acceptable approach to control plant diseases including CBB. To examine the main and interaction effects of the previously identified CBB resistance quantitative trait loci (QTL) associated with markers BC420 (B) on Pv06, SAP6 (S) on Pv10 and PVctt001 (P) on Pv04, in presence of the major QTL associated with the marker SU91 (Su) on Pv08, a resistant F4:5 recombinant inbred line population of the reciprocal crosses between OAC Rex (bb/ss/PP/SuSu) and HR45 (BB/SS/pp/SuSu) was evaluated under artificial field inoculation in disease nurseries in 2009 and 2010. While, in presence of the CBB QTL on Pv08, the QTL on Pv06 of HR45 accounted for 37–46% of phenotypic variation in the field, the effect of QTL on Pv04 and Pv10 were not significant under field conditions, even in the absence of the Pv06 QTL. Broad‐sense heritability estimates of CBB resistance and the QTL associated with BC420 were high for CBB severity and the area under the disease progress curve, promoting the continued efforts in pyramiding the QTL on Pv06 and Pv08 in common background, which provide high levels of resistance.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".