Phytotoxin of rice aggregate sheath spot pathogen <i>Rhizoctonia oryzae-sativae</i> and its biological activities
Bibliographic record
Abstract
Rice aggregate sheath spot disease occurs in many countries and causes serious yield losses. In China, the disease-causing fungus Rhizoctonia oryzae-sativae was reported in 1985, and since then, it has rarely been reported in major rice-growing areas after almost 30 years. Compared with Rhizoctonia solani, R. oryzae-sativae has a significantly different physiological morphology and growth status, although both fungi affect rice leaves in very similar ways. The optimum temperature for the suitable growth of R. oryzae-sativae is 31 °C, which is consistent with previous reports. We extracted phytotoxins from R. oryzae-sativae and analyzed its biological activity via the detached leaf and radicle inhibition methods. Rhizoctonia solani and R. oryzae-sativae exhibit differences in terms of pathogenicity and toxin activity, which indicates that these fungi may produce different toxin components. Based on gas chromatography–mass spectrometry data, esters, phenols, and other components were present in the crude toxin extract of R. oryzae-sativae. Our research provides a new method for studying the phytotoxins of R. oryzae-sativae. However, further studies are needed to elucidate the pathogenic mechanisms responsible for aggregate sheath spot disease in rice.
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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 | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".