Molecular Diagnostics and Pathogenesis of Fungal Pathogens on Bast Fiber Crops
Bibliographic record
Abstract
Bast fibers and products derived from them are undergoing a resurgence in demand in the global market. However, fungal diseases have become an important factor limiting their yield and quality, causing devastating consequences for the production of bast fiber crops in many parts of the world. Thus, there is a high demand for effective control and prevention strategies against fungal pathogens. Having rapid, specific, sensitive, and cost-effective tests that can be used for early and accurate diagnosis of disease agents is an essential step of such strategies. The objective of this study was to review the current status of research on molecular diagnosis of fungal pathogens on bast fiber crops. Our search of PubMed identified nearly 20 genera of fungal pathogens on bast fiber crops, among which the five most common genera were Colletotrichum, Pythium, Verticillium, Fusarium, and Golovinomyces. The gene regions that have been used for molecular identifications of these fungi include internal transcribed spacer (ITS), translation elongation factor 1-α (EF-1α), ß-tubulin, calmodulin (CAL), histone subunit 3 (H3), glyceraldehydes-3-phosphate dehydrogenase (GAPDH), etc. We summarize the molecular assays that have been used to identify these fungi and discuss potential areas of future development for fast, specific, and accurate diagnosis of fungal pathogens on bast fiber crops.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".