Development of a multiplex classical polymerase chain reaction technique for detection of <i>Didymella bryoniae</i> in infected cucumber tissues and greenhouse air samples
Bibliographic record
Abstract
Two sets of primer pairs were evaluated for their usefulness in detecting and identifying Didymella bryoniae (anamorph Phoma cucurbitacearum), causal agent of gummy stem blight, in infected cucumber plant tissues and air samples, using polymerase chain reaction. One primer in each pair was a universal primer that has been used for amplification of septate fungi. The other primer in each pair was an oligonucleotide designed from sequence information in the GenBank database to be specific to D. bryoniae. Each primer pair on its own was specific to genus level, but gave some nonspecific results for other Phoma or Didymella species. However, when these primer pairs were combined in a multiplex PCR, a unique result for D. bryoniae was obtained. The multiplex PCR gave positive results for cucumber tissue infected with D. bryoniae, and negative results for uninfected tissue and for tissue infected with Botrytis cinerea. Positive results were obtained from air samples collected with a rotation impaction sampler situated in a greenhouse containing cucumber plants infected with gummy stem blight. This method is rapid, sensitive, and accurate for detecting and identifying D. bryoniae in pure culture and plant tissue and could be applied to the detection of this organism in air samples. This method, therefore, could be a valuable technique for use in disease prediction.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".