Multiple virus and viroid detection and strain separation via multiplex reverse transcription – polymerase chain reaction
Bibliographic record
Abstract
Vegetatively propagated plants free from virus- or viroid-induced diseases are integral of the success of many agricultural and horticultural sectors. Reverse transcription – polymerase chain reaction (RT–PCR) has emerged as an effective method to detect the presence of causal agents and has been used as a management strategy to ensure virus- or viroid-free propagation. Advances in understanding virus and viroid genome structures and the availability of genomic sequences of viruses and viroids (and their strains) have made it possible to create primers for specific RT– PCR. This has permitted RT–PCR to evolve into a technique that can simultaneously detect multiple pathogens in a single sample. Multiplex RT–PCR can reduce cost and save time. A further modification of multipex RT–PCR, termed competitive RT–PCR, with a single-antisense and multiple-sense primers, has been developed. It facilitates simultaneous identification and differentiation of a group of virus strains. The methodology of multiplex RT–PCR and its modified (competitive) RT–PCR version for potato virus and viroid detection and strain differentiation are reviewed here.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".