Biosurveillance of oak wilt disease in Canadian areas at risk
Bibliographic record
Abstract
Biosurveillance of invasive species is critical for protecting native ecosystems and limiting economic losses. Early detection of pathogens through qPCR methods has recently shown great promise and can potentially slow the spread of devastating diseases. For instance, oak wilt, a disease caused by the fungus Bretziella fagacearum, can kill mature trees within weeks of infection. Originally contained in the United States, oak wilt has finally made its way into Canada, where it was recently observed for the first time in June 2023. This study has laid the foundations for a biosurveillance monitoring program of B. fagacearum in Eastern Canada. From 2019 to 2021, insect vectors were baited and captured in Lindgren traps in various locations of interest, namely sawmills importing oak logs from the United States (US), forested areas containing mature oak trees and strategic sites along the border between the two countries. Insect vectors and collection fluids were analysed with our qPCR detection test for the presence of B. fagacearum. As a positive control to validate this method, we included traps in a known centre of oak wilt infection in Michigan (US). Our analysis showed only one positive site at the border between Ontario (CA) and the US, even though oak wilt has never been observed there. This result confirms that DNA from B. fagacearum can be detected with this method even before the appearance of symptomatic trees, which could be crucial in the current containment efforts in Ontario (CA).
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".