A duplex droplet digital PCR assay for quantification of <i>Alternaria</i> spp. and <i>Botrytis cinerea</i> on sweet cherry at different growth stages
Bibliographic record
Abstract
Sweet cherries (Prunus avium) are an economically important crop in British Columbia, Canada. Cherries are harvested and distributed locally and overseas, where seemingly healthy fruit can succumb to postharvest diseases if disease conditions are met. Disease mitigation includes pre-harvest controls such as disease prediction models, disease monitoring, and fungicide applications. Development of disease-prediction models requires an understanding of how host and environmental conditions can affect the quantity of pathogens; therefore, quick, sensitive and accurate methods for pathogen quantification are required. This study has identified Alternaria spp. and Botrytis cinerea as major contributors to sweet cherry rot in Kelowna, British Columbia, in 2016 and developed a novel duplex droplet digital PCR assay for the rapid, concurrent quantification of the two pathogens. The assay involves the amplification of two abundant target regions, the internal transcribed spacer, and the intergenic spacer, in Alternaria spp. and B. cinerea, respectively. The detection limit was 0.1 pg of DNA for each target. The assay was validated during the 2016 and 2017 growing seasons at the bud break (2017 only), full bloom, petal fall, onset of straw colour and harvest stages of sweet cherry. In general, pathogen quantities were lowest at petal fall and highest during late season. The method can be used in future studies to evaluate pathogen quantities during the growing season and to facilitate the development of disease-prediction models and mitigation practices for growers.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".