Interlaboratory Evaluation of <i>Bretziella fagacearum</i> Molecular Detection Assays to Guide the <scp>eDNA</scp> Monitoring of Oak Wilt Disease
Bibliographic record
Abstract
ABSTRACT Oak wilt disease, caused by the fungus Bretziella fagacearum, can kill mature red oaks within months of infection, severely affecting biodiversity, landscapes, and industries. The disease, originally only present in the United States, was officially reported for the first time in Canada in June 2023. The aim of this study was to suggest a standardized assay and sample processing method to optimize oak wilt detection both in infection centers and ahead of the disease front. Two previously published molecular assays, a Nested PCR and a TaqMan qPCR, were compared to detect B. fagacearum in a variety of samples in a ring trial across five laboratories. Sample types investigated included eDNA from trapped insect vectors (sorted insects and bulk content from traps), infested and healthy oak wood chips, and B. fagacearum conidia dilutions. Results demonstrated that both Nested and TaqMan assays can be used for molecular confirmation of oak wilt, and results are reproducible across different labs. There is a general agreement between both detection assays when testing true‐positive and true‐negative samples. Both methods demonstrated overall good accuracy. The TaqMan assay was more sensitive and detected lower amounts of DNA target. Both tests were 100% specific to oak wood samples, which was the best sample type to use for detection. In general, samples with high Cts were more prompted to yield false negative Nested results. Detecting oak wilt from bulk insect samples was by far more rapid than sorted sap beetles, but resulted in lower detection signals, especially with the Nested assay. The time‐period when the insect traps were set up also had considerable influence on detection results. We hope this study helps to formulate guidelines in oak wilt detection and biosurveillance management.
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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 | 0.042 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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".