High Resolution Wildfire Fuel Mapping for Community Directed Forest Management Planning
Bibliographic record
Abstract
Climate change and institutional forest management practices are leading to more frequent and severe wildfire events around the world, a trend that is projected to increase in coming years. Wildfire plays an important role in maintaining ecological systems, but wildfires also pose threats to health, safety, infrastructure, and important ecosystem services. Reactionary response to these threats has predominantly informed management decisions in recent decades and greater focus on mitigation and adaptation is needed. Through a community directed consultation process, the goal of this work has been to provide direct, operational information to aid in local management decision making for a First Nations community in British Columbia, Canada. Here we use a combination of field sampling and high-resolution Airborne Laser Scanning (ALS) data to assess vertical and horizontal fuel loading at fine resolution (~10m2). Our analysis found a high degree of fuel loading heterogeneity in areas characterized as homogeneous using coarser fuel layers and provided a means of identifying high fire risk areas that may be targeted for ecosystem rehabilitation aimed at reducing current and future fire risk. We discuss how this spatially explicit data can be used to evaluate feedback between forest dynamics and fuel loading; information critical for managing forests for multiple objectives into the future. Following our analysis, we compiled our results for the community into an interactive decision support web mapping platform designed with the goal of user friendly, accessible land managment planning, avoiding the need for technical expertise and internal capacity.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".