Coarse-scale population structure of pathogenic<i>Armillaria</i>species in a mixed-conifer forest in the Blue Mountains of northeast Oregon
Bibliographic record
Abstract
The coarse-scale population structure of pathogenic Armillaria (Fr.) Staude species was determined on approximately 16 100 ha of relatively dry, mixed-conifer forest in the Blue Mountains of northeast Oregon. Sampling of recently dead or live, symptomatic conifers produced 112 isolates of Armillaria from six tree species. Armillaria species identifications done by using a polymerase chain reaction based diagnostic and diploiddiploid pairings produced identical results: 108 of the isolates were Armillaria ostoyae (Romagn.) Herink and four were North American Biological Species X (NABS X). Five genets of A. ostoyae and one of NABS X were identified through the use of somatic incompatibility pairings among the putatively diploid isolates. Armillaria ostoyae genet sizes were approximately 20, 95, 195, 260, and 965 ha; cumulative colonization of the study area was at least 9.5%. The maximum distance between isolates from the 965-ha A. ostoyae genet was approximately 3810 m, and use of three estimates of A. ostoyae spread rate in conifer forests resulted in age estimates for the genet ranging from 1900 to 8650 years. Results are discussed in relation to possible mechanisms that influenced the establishment, expansion, and expression of these genets; the genetic structure and stability of Armillaria; and the implications for disease management in this and similar forests.
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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 | 0.001 | 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 | 0.000 |
| 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".