Understanding wood decay diseases in western redcedar through development of ITS-sequencing and qPCR assays
Bibliographic record
Abstract
Abstract Root and butt rot diseases cause high rates of wood decay in living stands of western redcedar (WRC; Thuja plicata Donn), one of the most valuable forest species in western North America. However, WRC susceptibility and the virulence of wood-decay-causing fungal pathogens are understudied, presenting a high risk for the WRC forest industry. To evaluate susceptibility of WRC to root and butt rot diseases and decay incidence, four pathogenic fungi, including Armillaria ostoyae , Coniferiporia weirii , Heterobasidion occidentale , and Perenniporia subacida , were used to inoculate WRC seedlings using two artificial methods. Next-generation sequencing (NGS) of internal transcribed spacer (ITS) region of the nuclear ribosomal DNA (rDNA) and quantitative polymerase chain reaction (qPCR) assays were developed to evaluate successful infection through detection of targeted pathogens inside the WRC tissues while development of disease was assessed by visual observation of wood decay. Disease incidence rates ranged from 20% to 60% while infection rates ranged from 80% to 100%, validating the effectiveness of the inoculation protocols. The qPCR assays designed with species-specific primers were validated for quantification of absolute abundance of C. weirii inside WRC host tissues with high sensitivity and specificity. These newly developed qPCR assays provide rapid, cost-effective, and accurate tools to detect early infection and latent infection in asymptomatic trees, with wide potential applications for surveillance of C. weirii -caused wood decay disease in greenhouse and field studies, as well as for screening of disease resistance in WRC breeding.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 0.001 |
| 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".