Modeling infection and spread of <i>Heterobasidion annosum</i> in even-aged Fennoscandian conifer stands
Bibliographic record
Abstract
Heterobasidion annosum (Fr.) Bref. s. lato causes severe damage to forests. This study describes a model for simulating the infection and spread of Heterobasidion spp. in stands of Picea abies (L.) Karst. and Pinus sylvestris L. The model includes submodels for stand dynamics (plot simulation, growth and yield, and cross-cutting of trees) and disease dynamics: (i) spore infection, (ii) stump colonization, (iii) colonization of stump roots, (iv) vegetative transfer to tree roots, (v) spread of disease in tree roots, (vi) spread of decay in stems, and (vii) disease effect on tree growth and survival. To illustrate the model, a simulation was carried out on a Swedish Norway spruce stand where logging occurred during the high-risk season for spore infection. The model provides a means of incorporating the impact of root disease into forest planning. A change of parameters indicated that model predictions were sensitive to the spread rate and presence of initial disease centers. Further research is called for in areas related to the development of coarse root systems of various tree species and the probability and rate of transfer of disease from various sources of inoculum to healthy trees.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".