Identification and characterization of <i>Colletotrichum</i> species associated with anthracnose disease of banana
Bibliographic record
Abstract
Abstract Banana ( Musa spp.) is one of the five most abundantly produced fruits in the world and is widely planted in tropical and subtropical areas. Banana anthracnose is one of the main diseases during the growth and postharvest storage period of banana, seriously affecting quality and production. In this study, 24 samples of banana anthracnose were collected near the cities Nanning, Qinzhou, Baise, and Chongzuo in Guangxi Province, China. Based on colony features, conidial and appressorial morphology, and sequence analysis of several genomic regions (internal transcribed spacer [ITS] region, glyceraldehyde‐3‐phosphate dehydrogenase [ GAPDH ], actin [ ACT ], β‐tubulin [ TUB2 ], chitin synthase [ CHS ‐ 1 ], calmodulin [ CAL ], and the intergenic region of apn2 and MAT1‐2‐1 [ApMAT]), the 32 Colletotrichum isolates obtained were identified as five species: C . fructicola (41%), C . cliviicola (28%), C . siamense (16%), C . karstii (9%), and C . musae (6%). A conidial suspension (10 6 spores/ml) was used to inoculate banana seedlings for pathogenicity tests by applying 20 μl to wound sites. Lesions caused by C . musae developed most rapidly while those of C . karstii took the longest time to develop. This is the first report of C . siamense , and C . karstii associated with banana anthracnose in China, and the first report of C . fructicola and C . cliviicola associated with banana anthracnose worldwide.
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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 | 0.001 |
| 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".