Characterizing the effects of dwarf mistletoe and other diseases for sustainable forest management
Bibliographic record
Abstract
Many insects, fungi, and plants in forest ecosystems can damage trees and forests, depending on stand and environmental conditions. Natural disturbances, harvesting, and other forest practices can retard or increase the spread and the effects of dwarf mistletoe and other diseases on tree growth. To monitor the effects of diseases, certification and monitoring programs typically use incidence and severity of infestations as criteria and indicators. However, these are often insufficient to characterize the impact of the disease or to measure the effects of new management practices, such as variable retention silviculture, on sustainability. Long-term observations and models of stand development are advocated as better methods for characterizing disease effects. For dwarf mistletoe (Arceuthobium tsugense), we are designing and monitoring installations in infested stands of western hemlock (Tsuga heterophylla) and constructing a spatial and life history model of stand and disease development. Disease spread and effects are influenced by several factors including site quality, stand density, and the spatial arrangement of infected trees, which are sources of mistletoe spread into new stands. Potentially, these factors could be manipulated to either reduce or encourage the spread and the effects of dwarf mistletoe.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".