Initial tree mortality and insect and pathogen response to fire and thinning restoration treatments in an old-growth mixed-conifer forest of the Sierra Nevada, California
Bibliographic record
Abstract
Fire and thinning restoration treatments in fire-suppressed forests often damage or stress leave trees, altering pathogen and insect affects. We compared types of insect- and pathogen-mediated mortality on mixed-conifer trees 3 years after treatment. The number of bark beetle attacked trees was greater in burn treatments compared with no-burn treatments, and in some cases, larger pine trees were preferentially attacked. Restoration treatments are not expected to change the trajectory of spread and intensification of dwarf mistletoe. Thinning treatments may have provided a sanitation effect in which large leave trees have lower levels of dwarf mistletoe. Although thinning treatments are known to exacerbate root disease, <12% of cut stumps were infected with root pathogens ( Armillaria gallica and Heterobasidion annosum ). Treatments increased Ribes (alternate host for white pine blister rust) frequency and abundance, which may have very localized impacts on white pine blister rust dynamics. In some instances, fire, insects, and pathogens appear to conflict with forest restoration goals by reducing the percentage of pine and producing proportionally higher rates of tree mortality in large-diameter size classes. To better understand the long-term effects of restoration treatments on pathogens and insects, continued monitoring over the course of varying climatic conditions will be needed.
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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".