Isolate Neoscytalidium dimidiatum fungal pathogens from pytaya (Hylocereus undatus) and research controlling by microorganisms
Bibliographic record
Abstract
In recent years, Neoscytalidium dimidiatum has caused severe white spot disease in Pytaya, while no effective controls have been taken. In this study, two strains of N. dimidiatum NdGV and NdBT were obtained by isolation on water agar medium containing streptomycin, morphological tests, in vitro and in vivo pathogenical tests, and molecular biology tests by sequencing the genes ITS1 and ITS4. By using dual culture technique on potato-glucose agar medium, 100% of Trichoderma spp., 75% of Bacillus spp. and 20% of Streptomyces spp. were able to antagonize N. dimidiatum. The mean antagonistic effect with N. dimidiatum of Trichoderma spp. was higher than Bacillus spp. and the lowest was Streptomyces spp. 56.8%, 55.3% and 54.3% respectively. Especially 5 strains Trichoderma sp. 8.3.5, 8.3.7, 8.3.14, 8.3.19, and 8.3.20 had antagonistic effects of over 60%. The application potential of the 5 selected Trichoderma strains to control N. dimidiatum disease was further strengthened when their antagonistic effect was relatively stable on Pitaya juice agar medium while all Bacillus sp. and Streptomyces sp. were lost the ability to antagonize. It was noteworthy that four of the five strains of Trichoderma sp. were highly compatible, suggesting further studies are needed to apply their combined potency in enhancing the control of N. dimidiatum NdBT and NdGV on Pitaya.
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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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".