Development and Validation of Molecular Assays for Detection of Root- and Butt-Rot Diseases in Western Redcedar
Bibliographic record
Abstract
Western redcedar (WRC; Thuja plicata) root- and butt-rot diseases are caused by a set of wood-decay fungal pathogens, which have posed a significant threat to forest health and resulted in substantial economic losses of WRC production. Traditional approaches for disease detection are labor-intensive and more suitable on mature trees at late infection stages. This study developed and validated internal transcribed spacer region next-generation sequencing (ITS-NGS) and quantitative polymerase chain reaction (qPCR) assays for detecting decay-disease infections in WRC seedlings with high sensitivity and specificity. The efficiencies of ITS-PCR amplification were in silico predicted and validated through ITS-NGS using a pure fungal DNA mixture. For diagnosis of decay pathogens in WRC seedlings, fine root and root collar samples were collected from greenhouse inoculation trials. ITS-NGS identified positive infection rates of 100% for Armillaria ostoyae, Heterobasidion occidentale, and Poriella subacida in diseased seedlings, but the diagnostic efficiency for Coniferiporia weirii was affected by the types of sampled tissues. Species-specific qPCR assays were developed for C. weirii and revealed positive infection rates up to 100% in inoculated seedlings. Relative fungal abundances measured by ITS-NGS and qPCR were highly comparable, with significant correlation, demonstrating the reliability of both molecular diagnostic approaches. Moreover, qPCR provided higher quantification accuracy for a trace amount of the pathogen in total DNA extracted from host tissues. These results provided evidence for the application of ITS-NGS and qPCR assays as robust and reliable molecular tools for detecting early and latent infections of fungal pathogens in complex tissue samples for enhancing WRC disease management. [Formula: see text] Copyright © 2026 His Majesty the King in Right of Canada, as represented by the Minister of Natural Resources Canada. This is an open access article distributed under the CC BY 4.0 International license .
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".