Avoiding false positives in PCR-based identification methods for nonsterile plant pathogens
Bibliographic record
Abstract
Molecular approaches to species-specific identification are frequently applied in diagnostic, epidemiological, and phylogenetic studies. Many of these methodologies, such as polymerase chain reaction (PCR), random amplified polymorphic DNA, and restriction-fragment length polymorphism assays, make use of universal-primer sequences during the development of the assays or even in routine applications. This practise, particularly with environmental samples, can lead to erroneous conclusions, because samples may be contaminated with a wide range of diverse DNA sequences. As an example, in the course of developing PCR-based diagnostics for common nematode plant pathogens, in this study, the 18S–25S intragenic regions from genes encoding rRNA in the target organisms were isolated by PCR amplification. Employing “universal” primers, products of various sizes were amplified from DNA in six different pathogenic species, which had been isolated from the field and identified morphologically. While the observed size heterogeneity was very promising with respect to molecular identification, subsequent sequence analyses revealed several examples of contaminating DNA. Some reactions with universal primers did amplify rDNA sequences from the target nematodes but other reactions preferentially amplified rDNA sequences from small amounts of contaminating organisms, which were associated with the nematode samples. These results illustrate the necessity for sequence analyses when product size differences are adopted to identify closely related organisms and sampling cannot be carried out under sterile conditions.
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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 | 0.017 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".