Assessing the Use of DNA Detection Platforms Combined with Passive Wind-Powered Spore Traps for Early Surveillance of Potato and Tomato Late Blight in Canada
Bibliographic record
Abstract
Quantitative PCR (qPCR), loop-mediated amplification (LAMP), and lateral flow strip-based recombinase polymerase amplification (RPA-LFS) assays were assessed for early detection of Phytophthora infestans, the global causal agent of potato and tomato late blight, on passive wind-powered spore traps known as Spornados. Spore traps were deployed in potato and tomato fields during the 2018, 2019, and 2020 growing seasons in the provinces of Alberta, British Columbia, Manitoba, Prince Edward Island, and Ontario. All assays used DNA extracts from Spornado cassette membranes targeting the P. infestans nuclear ribosomal internal transcribed spacer. A total of 1,003 Spornado samples were qPCR tested, yielding 115 positive samples for P. infestans spores. In further assessment of these samples, LAMP detected P. infestans in 108 (93.9%) of 115 qPCR positive samples, and RPA-LFS detected it in 103 (89.6%). None of the assays showed cross-reaction with other Phytophthora species or pathogenic fungi known to infect potato and tomato. The qPCR detected ≤1 fg of P. infestans DNA, and LAMP and RPA-LFS amplified 10 fg in as little as 10 min. All assays detected P. infestans before the first report of late blight symptoms in commercial potato or tomato fields within each region or province. The combination of Spornado passive samplers with qPCR, LAMP, or RPA-LFS proved a valuable spore trapping system for early surveillance of late blight in potato and tomato. Both LAMP and RPA-LFS showed potential as alternative approaches to qPCR for in-field monitoring of P. infestans.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".