Relation between <i>Collybia fusipes</i> root rot and growth of pedunculate oak
Bibliographic record
Abstract
Collybia fusipes (Bull. ex Fr.) is the cause of a root rot on oak, mainly pedunculate oak, Quercus robur L., and northern red oak, Quercus rubra L. The disease is associated with a deterioration of the crown of affected trees in some, but not all, stands. We investigated the relationship between the level of root damage induced by C. fusipes and past growth of the infected trees at four sites of pedunculate oaks and one site of red oaks in northeastern and central France. The severely infected oaks showed poor growth for 1550 years depending on the site. At one site where C. fusipes was not associated with a deterioration of the affected tree crowns, the basal area increments of severely root damaged oaks has nevertheless been poor for more than 30 years. Severe infection by C. fusipes was associated with a 3050% reduction of basal area increment in the last 10 years preceding the study at all the sites. In contrast, in all the sites, trees lightly damaged by C. fusipes had basal area increments similar to the undamaged trees. The data would be consistent with a disease that develops slowly on vigorous trees and affects their growth only late in the infection process.
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 | 0.001 |
| 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".