<scp>G</scp>eographic analysis of the impacts of mountain pine beetle infestation on forest fire ignition
Bibliographic record
Abstract
Abstract In British Columbia, Canada, the largest mountain pine beetle outbreak on record has resulted in changes to fuel complexes that may alter fire regimes. The goal of this study is to analyze the relative importance of mountain pine beetle infestations as a determinant of forest fire ignition density in British Columbia. Fire ignitions data for the years 2000 to 2007 were modelled with covariates (weather, topography, ignition source, and the nature of the mountain pine beetle infestation) in 1km by 1km cells in the Montane Cordillera ecozone. Kernel density estimation was conducted for each fire season to illustrate broad scale trends in fire occurrence and regression trees were used to analyze the relative importance of each covariate. Results indicate precipitation, temperature, and Seasonal Severity Rating were the most influential determinants of fire ignition densities. While mountain pine beetle covariates were of lesser importance, moderate stand level tree mortality was more important for predicting the highest modelled fire ignition density values than high or extreme level mountain pine beetle mortality. Elevated fire ignition density was also associated with forests attacked by mountain pine beetle both one and six years previous, while other years were less important predictors.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.004 | 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".