A Molecular Assay Allows the Simultaneous Detection of 12 Fungi Causing Fruit Rot in Cranberry
Bibliographic record
Abstract
Cranberry fruit rot (CFR) is arguably one of the most limiting factors of cranberry (Vaccinium macrocarpon) production throughout its growing areas. The disease is caused by a group of closely related fungi that require identification using long and cumbersome steps of isolation and microscopic observations of structural features. The objective of this study was to develop a molecular assay to simultaneously detect and discriminate 12 of the most important fungal species reported to be pathogenic on cranberry fruit to facilitate the diagnosis of CFR. As the first approach, internal transcribed spacers and large subunit regions of all fungi were sequenced and confirmed with sequences available in the NCBI database. These data were used to develop primers able to differentiate seven of the 12 species. The five remaining species, including three in the Phacidiaceae family and two in the Glomerellaceae family, were differentiated on the basis of a more discriminant marker, the translation elongation factor 1-α. Two PCR reactions were optimized to clearly delineate the 12 species. The multiplex test was first validated using pure fungal cultures; it was subsequently validated using fruit collected in cranberry beds in eastern Canada. In the latter case, the test was rigorous enough to clearly discriminate the fungal pathogens from contaminants. Within the tested samples, Physalospora vaccinii and Coleophoma empetri were most commonly found. This molecular test offers scientists, diagnosticians, and growers a powerful tool that can rapidly and precisely identify fungi causing CFR so they can implement appropriate control methods.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".