Using a GFP-labeled Stagonospora nodorum strain as a DNA extraction efficiency standard in plant disease diagnosis
Bibliographic record
Abstract
Abstract A Stagonospora nodorum strain named DPGZL-2023 was created by transferring a green florescent protein (GFP) gene into the genome of the S. nodorum strain Sn15. DPGZL-2023 showed a similar pathogenicity as Sn15 but carried a strong GFP activity. A qPCR primers/probe set named P-GFP, targeting the GFP sequence, was designed. Using P-GFP, qPCR analysis was conducted on DNA extracted from replicated samples of DPGZL-2023 conidia, and confirmed that DPGZL-2023 could be used to characterize the variation in replicated DNA extractions. Conidia of DPGZL-2023 were used to spike soil samples inoculated with the canola clubroot pathogen Plasmodiophora brassicae, canola stem samples infected with the blackleg pathogens Leptosphaeria biglobosa and/or L. maculans and wheat/barley samples infected with Xanthomonas translucens pv. translucens (Xtt) or X. translucens pv. undulosa (Xtu). Duplex qPCR using P-GFP and a primers/probe set specific to P. brassicae, triplex qPCR using P-GFP and primers/probe sets specific to L. biglobosa and L. maculans, and triplex qPCR using P-GFP and primers/probe sets specific to Xtt and Xtu were conducted. The results indicated that DPGZL-2023 could be used as a standard for DNA extraction efficiency in qPCR-based plant disease diagnosis. Adding DPGZL-2023 conidia to plant or soil samples prior to DNA extraction, and subsequent use of the P-GFP detection control, provided an added control that could distinguish truly negative from false-negative qPCR results.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.005 |
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".