Multiplex PCR Assay for Rapid Identification of <i>Monilinia rubi</i>, the Causal Agent of Dry-Berry Disease of Caneberries
Bibliographic record
Abstract
Monilinia rubi is the causal agent of dry-berry disease of raspberry and blackberry in northern Washington state and western Canada. The symptoms are visible on green fruits and include necrotic and dried drupelets with progressive necrosis from the receptacle into the peduncle. Diagnosis is based on symptoms, isolation, and identification of the slow-growing fungal pathogen. Diagnosis is slow and difficult with late-season tissues because abiotic stresses or other diseases may cause similar symptoms, and the slow-growing pathogen is not easily isolated from tissues harboring fast-growing environmental fungi. A multiplex PCR assay with primers to amplify an ITS region and beta-tubulin was designed to provide a rapid method to identify the pathogen in culture and in infected berry tissues. For M. rubi and infected berries, two amplicons that differ in length by 400 bp are visualized on agarose gels. No bands were obtained from fungal outgroups or nonsymptomatic berries. For further confirmation of the pathogen and the disease, a single amplicon can be sequenced directly from the multiplex reaction and compared with reference sequences in GenBank. This rapid multiplex assay streamlines diagnosis of dry-berry disease, and its application could provide valuable information on the range of the pathogen, especially in other caneberry production regions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".