Molecular detection of <i>Fusarium subglutinans</i> , the causal organism of internal fruit rot in greenhouse peppers
Bibliographic record
Abstract
Internal fruit rot of sweet peppers, caused by Fusarium subglutinans is a new disease found in commercial greenhouses in British Columbia and Alberta, which causes considerable yield losses. Experiments were conducted to develop a rapid and accurate assay for detection of F. subglutinans, by dot-blot hybridization. Internal transcribed spacers 1 (ITS1) and 2 (ITS2) and 5.8S rDNA were amplified by polymerase chain reaction, using universal primers, and a dot-blot assay was employed to detect the pathogen in culture and in the host. Among six probes tested, three (Fsub-1, Fsub-3, and Fsub-5) were selected because they differentiated F. subglutinans from other Fusarium spp. and greenhouse pathogens. Probe Fsub-3 hybridized with all F. subglutinans isolates. Fsub-1 hybridized only with F. subglutinans isolates from Monterey pine (Pinus radiata). Fsub-5 hybridized with F. subglutinans isolates from corn (Zea mays), peppers (Capsicum annuum), and Panicum miliaceum, but not with Pinus radiata isolates. None of these primers hybridized with Fusarium spp. other than F. subglutinans or with other pathogens of greenhouse crops. Dot-blot hybridization developed in this study differentiates F. subglutinans from other Fusarium spp. as well as from other fungal pathogens causing fruit rot of peppers. This technique may help to detect and identify F. subglutinans in culture and in pepper fruits.
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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 | 0.000 |
| 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".