Distribution of fungal foliage and shoot pathogens in a natural Scots pine population in relation to environmental variables
Bibliographic record
Abstract
The development of fungal diseases within plant populations is simultaneously affected by spatially varying factors, which include environmental variables as well as characteristics of the host population itself. We studied the effects of such variation by investigating the relationships between several environmental factors and the occurrence and abundance of pathogenic fungi (Phacidium infestans L., Gremmeniella abietina (Lagerb.) Morelet, and Lophodermella sulcigena (Rostr.) Höhn) in a naturally regenerated, postfire population of Scots pine (Pinus sylvestris L.) saplings. The fieldwork was carried out in the pristine forest of Kostamuksha Strict Nature Reserve in the Karelian Republic, Russia. The microhabitat as well as the density and height of the saplings were factors that were related to the extent of damage caused by fungal diseases. However, the factors were correlated to the amounts of single pathogen species in different ways. Most obviously, microhabitat was related to the amount of G. abietina, while the amount of P. infestans was positively correlated with the increasing density of the stand. Lophodermella sulcigena was most abundant on tall saplings. Gremmeniella abietina occurred together with the other two fungi less frequently than would be expected from random occurrence, suggesting different ecological requirements or competitive exclusion. Fungal diseases caused considerable mortality and damage in the Scots pine population in question. We suggest that pathogenic fungi affecting shoots and foliage may affect the spatial distribution and regeneration dynamics of natural Scots pine populations.
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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 | 0.000 |
| 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".