Influence of mountain pine beetle outbreaks on large fires in British Columbia
Bibliographic record
Abstract
Abstract A key uncertainty in understanding climate change effects on wildfires in western North America is the role of mountain pine beetle (MPB) outbreaks in driving wildfire occurrence and severity. In this study, we investigated the complex relationship between MPB outbreaks, other environmental factors, and wildfire occurrence in British Columbia (BC), Canada. We adopted a fire risk analysis method developed for fire occurrence prediction to separate the effect of changing fuel conditions on wildfires in BC when neither post‐outbreak fuel conditions, climate, nor management is stationary. Using lasso‐logistic regression and a novel variable ranking procedure, we determined that MPB‐affected areas had 1.7 times more large lightning‐caused fires (≥100 ha), as the likelihood of large lightning‐caused fires increased by 40% in these areas and likely contributed to the increased burned areas in BC. Meanwhile, the likelihood of large human‐caused fires decreased in MPB‐affected areas. Fire weather factors were most influential for both lightning‐ and human‐caused fires, while anthropogenic factors were most influential for human‐caused fires. Fuel dynamics following MPB outbreaks vary across the wide distribution of a host species such as lodgepole pine, at stand and landscape levels. Furthermore, the expression of the effects of MPB and other disturbances on wildfire is also conditional on, as well as confounded with, many other environmental factors and management activities that vary across western North America. Therefore, a lack of consensus on the impacts of MPB on wildfire is not surprising.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.007 | 0.003 |
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; both teacher heads agree on what is shown here.
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".