Interactions among fire, insects and pathogens in coniferous forests of the interior western United States and Canada
Bibliographic record
Abstract
Abstract 1 Natural and recurring disturbances caused by fire, native forest insects and pathogens have interacted for millennia to create and maintain forests dominated by seral or pioneering species of conifers in the interior regions of the western United States and Canada. 2 Changes in fire suppression and other factors in the last century have altered the species composition and increased the density of trees in many western forests, leading to concomitant changes in how these three disturbance agents interact. 3 Two‐ and three‐way interactions are reviewed that involve fire, insects and pathogens in these forests, including fire‐induced pathogen infection and insect attack, the effects of tree mortality from insects and diseases on fuel accumulation, and efforts to model these interactions. 4 The emerging concern is highlighted regarding how the amount and distribution of bark beetle‐caused tree mortality will be affected by large‐scale restoration of these fire‐adapted forest ecosystems via prescribed fire. 5 The effects of fire on soil insects and pathogens, and on biodiversity of ground‐dwelling arthropods, are examined. 6 The effects of fire suppression on forest susceptibility to insects and pathogens, are discussed, as is the use of prescribed fire to control forest pests.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 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".