Bibliographic record
Abstract
The root system is an important part of a tree, but being inconspicuous it does not attract much attention. Therefore, root diseases are often invisible during much of the pathogens' life cycle. Nevertheless, they cause important losses due to mortality and reduced growth, but they are also components of forest ecosystems. For example, the complex of Armillaria root diseases, frequently associated with Armillaria ostoyae, attacks stressed trees and can kill seedlings or reduce the growth of trees during decades while hidden in roots. Inonotus tomentosus is common in spruce. From our observations, the pathogen usually infects trees older than 30 years; conifer seedlings planted on an infested site do not show any disease symptoms even after 10 years. Again, I. tomentosus needs stressed trees to develop in a stand. Heterobasidion annosum is an aggressive pathogen of pine. It colonizes fresh pine stumps after thinning in pine stands and can kill surrounding trees. Through lack of knowledge, foresters can create conditions conducive to the spread of root diseases: for example, precommercial thinning in the boreal forest has promoted Armillaria root diseases. Thinning in red pine plantations also creates an ecological niche favourable to H. annosum. Conversely, high-density spruce plantations will trigger Tomentosus root rot infestations. Proactive forest protection needs to integrate knowledge of silviculture, ecology, entomology and pathology. Understanding the ecology of these fungi is the first step.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".