<i>In Situ</i> Processing and Efficient Environmental Detection iSPEED of pests and pathogens of trees using point-of-use real-time pcr
Bibliographic record
Abstract
Abstract The increase in global trade is responsible for a surge in foreign invasive species introductions across the world. Early detection and surveillance activities are essential to prevent future invasions. Molecular diagnostics by DNA testing has become an integral part of this process. However, for environmental applications, there is a need for cost-effective and efficient point-of-use DNA testing that would allow for the collection of results in real-time away from laboratory facilities. To achieve this requires the development of simple and fast sample processing and DNA extraction, room-temperature stable reagents and a portable instrument. We conducted a series of tests using a crude buffer-based DNA extraction protocol and lyophilized, pre-made, reactions to address the first two requirements. We chose to demonstrate the use of this approach with organisms that cover a broad spectrum of known undesirable insects and pathogens: the ascomycete Sphaerulina musiva , the oomycete Phytophthora ramorum , the basidiomycetes Cronartium ribicola and Cronartium comandrae and the insect Lymantria dispar . Tests performed from either infected leaf material or spores (pathogens), or legs and antenna (insects). We were able to obtain positive amplification for the targeted species in all the samples tested. The shelf-life of the lyophilized reactions was assessed, confirming the stability of over a year at room temperature. Finally, successful tests conducted with portable thermocyclers and disposable plastics, demonstrating the suitability of the method, named in Situ Processing and Efficient Environment Detection (iSPEED), for field testing. This kit is ideally adapted to field testing as it fits in a backpack and can be carried to remote locations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".