Evaluating Potential Fire Behavior in Lodgepole Pine-Dominated Forests after a Mountain Pine Beetle Epidemic in North-Central Colorado
Bibliographic record
Abstract
Abstract A mountain pine beetle outbreak in Colorado lodgepole pine forests has altered stand and fuel characteristics that affect potential fire behavior. Using the Fire and Fuels Extension to the Forest Vegetation Simulator, potential fire behavior was modeled for uninfested and mountain pine beetle-affected plots 7 years after outbreak initiation and 10 and 80% projected tree fall using measured and projected fuel and stand characteristics. Under 90th percentile weather conditions, uninfested plots exhibited proportionally more crown fire than infested plots. Plots predicted to have crown fire were composed mainly of nonhost conifer species and had a lower and more continuous canopy than infested plots. Where surface fire was predicted to occur, live lodgepole pine was the only conifer present, and plots had significantly lower tree mortality from fire than plots predicted to have crown fire. Mountain pine beetle-induced changes in stand and fuel characteristics resulted in increased intensity of surface fire behavior. Furthermore, with 80% infested tree fall, potential smoke production was predicted to be higher. Tree species composition of stands pre and postbark beetle outbreak is important when identifying mountain pine beetle-caused changes to potential fire behavior.
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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 | 0.001 | 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) | 0.000 | 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".