Detection and quantification of airborne spores from six important wheat fungal pathogens in southern Alberta
Bibliographic record
Abstract
Wheat is affected by many fungal diseases that can cause severe yield and quality losses. Disease prediction models generally employ weather data to estimate potential for infection to determine timing for fungicide applications, but these models fail to account for the presence and quantity of pathogen inoculum. This study adapted highly specific qPCR primers to identify and quantify, in real-time, inoculum present in air for the six most important wheat pathogens in Canada. Fungal spores were collected using either Burkard spore collectors and quantified using qPCR or microscope slides covered with adhesive tape and identified and quantified using microscopy. Samples were collected from seven different sites in southern Alberta throughout the 2015–2017 growing seasons. The results demonstrated that qPCR can reliably identify and quantify spores from Puccinia striiformis f. sp. tritici, P. triticina, P. graminis f. sp. tritici, Blumeria graminis f. sp. tritici, Pyrenophora tritici-repentis, and Fusarium graminearum. The limits of detection of DNA for primer pairs in singleplex tests ranged from 0.0001 ng for P. graminis to 0.001 ng for P. tritici-repentis, which corresponded to approximately 3 spores for P. tritici-repentis and F. graminearum and 1 spore for the other pathogens. Conversely, microscopy permitted identification of rusts to the genus but not to the species level and was ineffective in quantification of the remainder of the wheat pathogens. This study will contribute to the development of a fast and reliable forecasting system that will enable identification and quantification of airborne pathogens in real-time before initial disease symptoms appear.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".