Wildland Fire Smoke and Emissions Tradeoff Decision Support
Bibliographic record
Abstract
In 2023, 6,551 wildfires across Canada burned 184,961 km2 of the landscape—about 5% of Canadian forests—emitting nearly 480 megatonnes of carbon, with emissions leading to air quality warnings as far away as Washington DC, USA. In early June, the air quality index in New York City was over 400, and by mid-June, smoke plumes passed above Europe. As wildland fires of increasing severity occur with increasing frequency, driven by global climate change and decades of fire suppression, societies near and far from high-risk ecosystems face increased exposure to wildfire emissions that may have both acute and long-term health impacts. Prescribed fire interventions show promise for reducing the risk of large wildfires in fire-prone ecosystems, but implementing prescribed fire can be difficult, in part due to concerns about the potential health impacts of prescribed fire smoke on nearby communities. To provide decision support for land managers aiming to reduce wildfire risk with prescribed fire treatments, we will produce a geospatial database of daily pollutant emissions and fire intensity from simulations of prescribed and wildland fires over a 20-year period for: 1) a baseline scenario of no management actions, 2) one or more scenarios of prescribed fire locations and timing based on interaction with tribes and Okanogan-Wenatchee National Forest (OWNF) managers, and 3) scenarios of prescribed fire locations and timing based on fire paths, locations of highly valued resources, areas available and suitable for treatment, determined by the research team. We can accomplish this by iterating between FSim, the Large Fire Simulator, which stochastically simulates large wildfire ignition and spread across a LANDFIRE fuels landscape, and FFE-FVS, the Forest Vegetation Simulator with the Fire and Fuels Extension, which simulates post-fire regeneration, forest growth, management actions including prescribed fire, fuel dynamics, and fuel consumption and pollutant emissions from prescribed fires and wildfires. Because FSim takes a Monte Carlo approach, simulating fires over 10,000 or more hypothetical fire seasons comprised of daily weather sequences, we will be able to estimate the probability of each landscape pixel burning in a wildfire and the conditional probability of that pixel burning at different flame lengths, allowing us to provide emissions estimates within a risk-assessment framework for managers. The framework will allow land managers to quantify the likelihood that smoke impacts from near-term prescribed fire treatments will be offset by reductions in severe smoke events from future wildfires. Additionally, the smoke event geospatial datasets may provide input into atmospheric transport models which could be used to simulate regional to national scale smoke impacts. We will pilot the project in Okanogan-Wenatchee National Forest, Washington, USA, working with the forest’s managers to design fuel treatment scenarios that will yield realistic fire occurrence trajectories and emission estimates to inform near-term prescribed fire operations. As a U.S. Federal Bipartisan Infrastructure Law Research & Development “proof of concept” project, the Wildland Fire Smoke and Emissions Tradeoff Decision Support project will inform U.S. Forest Service management policy and strategy around the use of prescribed fire in other National Forests in the U.S.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 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".