Bibliographic record
Abstract
Understanding the intricate interactions between rice ( Oryza sativa ) and its pathogens is crucial for developing effective disease management strategies. Transcriptomic approaches have significantly advanced our knowledge in this area by enabling comprehensive profiling of gene expression during infection. This study leverages high-quality RNA sequencing and other transcriptomic techniques to explore the dynamic interactions between rice and various pathogens, including the rice blast fungus ( Magnaporthe oryzae ) and the Rice black-streaked dwarf virus (RBSDV). Key findings include the identification of differentially expressed mRNAs and long non-coding RNAs (lncRNAs) that play essential roles in rice's defense mechanisms, as well as novel microRNAs (miRNAs) that regulate pathogen resistance genes. Additionally, tissue-specific expression patterns of pathogenicity genes and miRNAs were observed, providing deeper insights into the dual-epidemics of blast disease. These transcriptomic analyses offer a valuable resource for understanding the molecular mechanisms underlying rice-pathogen interactions and pave the way for developing improved disease-resistant rice varieties.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".