Quantification of seedborne infection by <i>Rhynchosporium secalis</i> in barley using competitive PCR
Bibliographic record
Abstract
The potential of the competitive polymerase chain reaction (PCR) assay for quantification of seedborne infection by Rhynchosporium secalis in barley was examined using a primer set (RS1 and RS3) derived from the internal transcribed spacer (ITS) regions of ribosomal RNA genes of this pathogen. Introduction of a heterologous internal control, which competes for the same primer set in the conventional PCR assay, allowed for detection and quantification of R. secalis fungal biomass. In order to generate a standard calibration curve, DNA prepared from infected seeds with different levels of R. secalis infection was subjected to competitive PCR assay. The resulting PCR product ratio for each PCR reaction ( R. secalis ‐amplified DNA/internal control template‐amplified DNA) increased proportionally with increasing levels of infected seed DNA in the reaction mixture. Naturally infected seed lots collected from 1995 to 1999 were used to demonstrate the potential of the competitive PCR assay as an alternative seed health testing method. The results from this competitive PCR assay were compared with those from conventional visual disease assessment and an agar plate assay. Although relatively good correlation between visual disease assessment and the competitive PCR was found in the case of artificially mixed seed samples, there was poor correlation in the experiments using naturally infected seed samples.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".