Evaluation of the effect of qSB-9<sup>Tq</sup> involved in quantitative resistance to rice sheath blight using near-isogenic lines
Bibliographic record
Abstract
Resistance to rice sheath blight disease that causes significant yield losses worldwide is controlled by multiple quantitative trait loci (QTL). The accurate resistance effect of qSB-9Tq, a major QTL that derived from the indica cultivar Teqing, was investigated by using a backcross population of Teqing and Lemont (the latter as the recurrent parent) and a set of near-isogenic lines (NIL) developed in this study. A strong pathogenic Rhizoctonia solani line, RH-9, was used for inoculation. Two different experimental designs were adopted. One was a completely randomized experiment in which plants that exhibited three different genotypes at qSB-9Tq locus were distinguished by detecting the marker genotypes flanking qSB-9Tq in the BC6F2 segregation population. The other was a randomized block design experiment with three replicates using the three NILs of qSB-9Tq. The combined results of the two experiments suggested that qSB-9Tq was a dominant resistance QTL and could reduce disease rating by approximately 1.0 using a 0~9 disease rating scale. The results of the study provide a strong foundation for the fine mapping of qSB-9Tq and its utilization in breeding.Key words: Near-isogenic line, quantitative trait locus, resistance effect, rice (Oryza sativa L.), sheath blight
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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) | 0.000 | 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 | 0.001 |
| 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".