Spread of <i>Heterobasidion</i> genotypes in Norway spruce stands on drained peat soil in Latvia
Bibliographic record
Abstract
According to several earlier studies, the prevalence of Heterobasidion in peat soils is generally lower compared to mineral soils. However, in some Norway spruce (Picea abies (L.) Karst.) stands on drained peat soils in Latvia, serious damage caused by Heterobasidion root rot has been observed. To determine the spread of Heterobasidion spp. on peat soil, we analyzed the structure of Heterobasidion genets in 20 study plots established in disease centres in 11 spruce-dominated peatland forest stands. A total of 381 standing spruce trees and 244 spruce stumps were examined for Heterobasidion infection. The fungus was isolated from 181 spruce trees (47.5%) and 43 stumps (17.6%). In total, 101 different Heterobasidion genotypes (genets) were identified (on average five genotypes per study plot). The average number of trees infected by a single Heterobasidion genotype was 2.2. Most of the genets (68.3%) had infected only one tree or stump while the rest of the genets (31.7%) had infected several trees and stumps. To the best of our knowledge, this is the first study to investigate the spread of Heterobasidion genotypes in peatland forest stands. To reduce losses caused by Heterobasidion root rot in spruce forests on drained peat soils, it is important to prevent primary spore infections as well as to avoid planting pure spruce stands with high density.
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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) | 0.000 | 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".