Development of STS markers and QTL validation for common bacterial blight resistance in common bean
Bibliographic record
Abstract
Abstract Common bacterial blight (CBB) of common bean ( Phaseolus vulgaris L.), is one of the major diseases that decrease yield and quality. A major quantitative trait locus (QTL) for CBB resistance from line XAN 159 was transferred into two bean lines, HR45 and HR67. Previous studies identified that two markers are linked to this QTL but the chromosome location was not consistent. To identify more tightly linked markers and to verify the chromosome location, 65 additional markers were mapped using 81 recombinant inbred lines (RILs) derived from a cross HR67 × OAC95‐4. The QTL was mapped to a 13 cM region on chromosome 1 and defined by eight molecular markers that explained 25–52% of the phenotypic variation. Six tightly linked amplified fragment length polymorphism markers (0.6–9.7 cM from the QTL peak) were converted into seven sequence tagged site markers, three of which were mapped to this QTL. Five tightly linked markers were used to screen 907 F 2 plants derived from a cross HR45 × ‘OAC Rex’ and four of them were linked to each other within 4.2 cM. These markers may be useful in marker‐assisted selection and map‐based cloning of this major QTL.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".