Selection of genetically diverse <i>Trichoderma</i> spp. isolates for suppression of <i>Phytophthora capsici</i> on bell pepper
Bibliographic record
Abstract
Environmentally compatible control measures are needed for suppression of Phytophthora capsici on pepper. Twenty-three isolates of Trichoderma were screened for suppression of a mixture of 4 genetically distinct isolates of this pathogen on bell pepper (Capsicum anuum) in greenhouse pot assays. Of these 23 isolates, GL12, GL13, and Th23 provided significant suppression of P. capsici in at least 2 assays. These isolates were then compared with Trichoderma virens isolates GL3 and GL21 for suppression of this disease in the presence and absence of the harpin-based natural product Messenger. Isolates GL3 and Th23 provided significant disease suppression (P ≤ 0.05) in 3 of 4 assays, while GL12, GL13, and GL21 provided significant suppression in 2 of 4 assays. There was no apparent benefit from the application of Messenger. Phylogenetic analysis of these 5 isolates (based on the ITS1 region of the nuclear rDNA cluster and tef1), and an additional 9 isolates that suppressed P. capsici in at least 1 assay, separated isolates into 2 clades, with 1 clade containing GL3, GL12, GL13, and GL21. There were also 2 more distantly related isolates, one of which was Th23. We report here the identification of genetically distinct Trichoderma isolates for potential use in disease management strategies employing isolate combinations directed at suppression of P. capsici on pepper.
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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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".