Bibliographic record
Abstract
Fire severity in mesic mixed-conifer forests is shaped by complex interactions among climate, weather, fuels, and topography, yet treatment effectiveness in these systems remains poorly quantified across broad landscapes. We evaluated drivers of burn severity and fuel treatment effectiveness across four Northern Rocky Mountain ecoregions (Canadian Rockies, Idaho Batholith, Middle Rockies, and Northern Rockies) using an empirical, landscape-scale modeling framework. We compiled burn severity, treatment history, and environmental predictors for 700 wildfires (2001–2023), and developed Random Forest models using both gridded weather (gridMET) and higher-resolution TopoFire predictors. Treatment effectiveness was evaluated using matched-control comparisons and a counterfactual approach that estimated expected severity under a no-treatment scenario. Across ecoregions, fire severity was most strongly regulated by climate and short-term weather conditions, particularly predictors related to fuel moisture (e.g., dead fuel moisture, soil moisture, and climate water deficit), while static topographic and fuel metrics played secondary, context-dependent roles. Elevation was the only topographic predictor consistently ranked among the strongest variables, likely reflecting covariation with bioclimatic gradients rather than independent controls on fire behavior. Incorporating higher-resolution TopoFire weather data modestly improved representation of within-fire variability but did not substantially improve model performance or transferability to independent fires, underscoring challenges in generalizing severity models across heterogeneous landscapes. Treatment effectiveness varied by treatment type, age, ecoregion, and evaluation method. Combined treatments that reduced both canopy and surface fuels—particularly removal + surface reduction and removal + prescribed fire—showed the most consistent reductions in severity. In several categories, older treatments occasionally reduced severity more than recent treatments, suggesting that treatment longevity in mesic forests may be mediated by fuelbed decomposition and moisture dynamics. Across the study extent, prior wildfire and prescribed fire comprised the largest treatment footprints within subsequent wildfire perimeters, indicating that wildfire itself is a dominant driver of landscape fuel modification. These findings highlight the context dependence of treatment outcomes in mesic forests and support evaluation frameworks that integrate counterfactual modeling and broader management objectives beyond severity reduction.
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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 | 0.002 | 0.003 |
| 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 | 0.001 | 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".