Controlled inoculation provides insight into western redcedar resistance to multiple root- and butt-rot pathogens
Bibliographic record
Abstract
Western redcedar (Thuja plicata Donn) is one of the most valuable forest species in western North America, but high incidence of root- and butt-rot diseases has resulted in large economic losses. During a long history of host–microbe co-evolution, redcedar has gained highly quantitative disease resistance to these pathogens compared to other conifer species. Despite this, genetic disease resistance has rarely been incorporated into redcedar breeding programs. This study evaluated redcedar resistance to the root- and butt-rot diseases caused by eight wood decay fungi. Two artificial inoculation methods, using wood block-stick and dowel-plug inoculums, were developed for infection of three-year-old seedlings under controlled greenhouse conditions. Disease symptoms and infection processes were assessed over 18 months post inoculation. Disease incidence rates ranged from 10% to 60% for five pathogens (Armillaria ostoyae, Coniferiporia weirii, Heterobasidion occidentale, Poriella subacida, and Postia balsamea). Among these, only C. weirii and Poriella subacida caused symptoms of both wood discoloration and decay. Infection processes varied among the five pathogens. The remaining three decay fungi (Porodaedalea pini, Postia sericeomollis, and Obba rivulosa) did not cause obvious disease symptoms. However, molecular diagnosis using next-generation sequencing of the internal transcribed spacer region (ITS-NGS) detected target pathogens in asymptomatic but inoculated seedlings. These latent infections were characterized by high incidence rates and intermediate levels of molecular infection severity (MIS), which significantly impaired seedling growth. The continuous MIS variation among asymptomatic seedlings highlights latent infection as a key quantitative trait for screening resistance in western redcedar. This study provides essential insights into disease development and latent infection in western redcedar, contributing to improved prediction of disease outbreaks, forest health management, and the development of early intervention strategies. The inoculation methods and molecular diagnostics established here offer valuable tools for integrating disease resistance into western redcedar breeding programs.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".