A Quantitative PCR System for Measuring <i>Sclerotinia sclerotiorum</i> in Canola (<i>Brassica napus</i>)
Bibliographic record
Abstract
Sclerotinia stem rot, caused by Sclerotinia sclerotiorum, is an economically important disease of canola (Brassica napus) commonly managed by routine application of fungicides. Petal infestation has been demonstrated to be an important stage of the disease cycle in canola and has been the focus of previously developed Sclerotinia stem rot risk assessment methods. Quantitative polymerase chain reaction (qPCR) analysis can provide a more rapid and accurate assessment of petal infestation levels. Primers and a hydrolysis probe were designed to amplify a 70-bp region of an S. sclerotiorum-specific gene, SS1G_00263. A hydrolysis probe-based qPCR assay was developed that had a detection limit of 8.0 × 10−4 ng of S. sclerotiorum DNA and only amplified S. sclerotiorum DNA. Evaluation of petals collected at five sampling points in each of 10 commercial canola fields on each of two sampling dates (corresponding to 20 to 30% bloom and 40 to 50% bloom) revealed S. sclerotiorum DNA infestation levels of 0 to 3.3 × 10−1 ng/petal. This qPCR assay can be used to reliably quantify petal infestation and, with further research, has the potential to serve as the basis for a Sclerotinia stem rot risk assessment tool or as a means to study Sclerotinia stem rot epidemiology.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".