Identification and validation of sheath blight resistance in rice (<i>Oryza sativa</i>L.) cultivars against<i>Rhizoctonia solani</i>
Bibliographic record
Abstract
Rice sheath blight, caused by Rhizoctonia solani, is a devastating disease of rice which causes major yield loss in most rice growing regions of the world. Hence, identification and subsequent development of disease resistance in rice cultivars is crucial. Six moderately resistant cultivars, namely ‘Teqing’, ‘Jasmine85’, ‘Tetep’, ‘Pecos’, ‘Azucena’ and ‘Taducan’, one susceptible local cultivar, ‘MR 219’, and two new advanced breeding lines, ‘UKMRC 2’ and ‘UKMRC 9’, were screened using micro-chamber and mist-chamber methods. The fungal isolate was confirmed as R. solani using ITS-rDNA sequencing. Severe sheath blight was recorded following inoculation with R. solani under micro-chamber conditions. The most resistant cultivar was ‘Tetep’, followed by ‘Teqing’. In mist-chamber screening, ‘UKMRC 2’ showed the highest level of susceptibility with a disease severity index (DSI) of 6.67, while ‘MR 219’ produced the highest DSI of 7.22 in the micro-chamber. Significant correlation of plant height and disease was obtained with relative lesion height (RLH) indices. Significant correlations were also observed among diseased plant affected area (DPAA), VRT (visual rating) and RLH, with VRT being the most accurate. On the basis of the disease reactions, ‘Tetep’ and ‘Teqing’ were identified as suitable donors to improve resistance in ‘UKMRC 2’ and ‘MR 219’. Mist-chamber screening method was more reliable to evaluate sheath blight under greenhouse conditions than the micro-chamber method.
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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 | 0.001 | 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 | 0.000 |
| 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".