Bibliographic record
Abstract
Sorghum is a very important food and energy crop in the world, but it is often affected by many diseases, such as anthracnose, grain mold, bacterial stripe disease, and pests like aphids. These problems will cause the yield of sorghum to decline and also affect its quality. In recent years, molecular biology and multi-omics techniques have developed rapidly, which has also helped us more clearly understand the disease resistance mechanism of sorghum. Current research indicates that sorghum defuses pathogens in multiple layers. It can first identify the signals related to pathogens and then transmit these signals, such as through MAPK or some hormone routes. Then, many disease-resistant genes will be activated in sorghum, including some NLR receptors, PR proteins, antimicrobial peptides, and 3-deoxyanthocyanins, etc. Meanwhile, the metabolic process of sorghum will also be rearranged, thereby enhancing its broad-spectrum resistance to fungi, bacteria and insects. The integration of multi-omics data (such as genomics, transcriptomics, and metabolomics) offers us a more comprehensive picture, which includes many complex regulatory networks, such as disease-resistant genes, signaling pathways, and various metabolites. Genome editing technologies, such as CRISPR/Cas9, as well as molecular marker-assisted selection, also make disease-resistant breeding more precise and efficient. The utilization of the microbiome to help sorghum defend against diseases or the application of some biological control methods is also regarded as very promising. Future research needs to integrate multi-omics and systems biology to conduct a more in-depth study on how sorghum defuses against the simultaneous infection of multiple pathogens. At the same time, it is also necessary to better integrate molecular breeding with traditional breeding to enhance the efficiency of selecting disease-resistant varieties and achieve more stable and sustainable disease management.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".