Cell2Fire: A Cell-Based Forest Fire Growth Model to Support Strategic Landscape Management Planning
Bibliographic record
Abstract
Cell2Fire is a new cell-based wildland fire growth simulator designed to integrate data-driven landscape management planning models. The fire environment is modeled by partitioning the landscape into cells characterized by fuel, weather, moisture content, and topographic attributes. The model can use existing fire spread models such as the Canadian Forest Fire Behavior Prediction System to model fire growth. Cell2Fire is structured to facilitate its use for predicting the growth of individual fires or by embedding it in landscape management simulation models. Decision-making models such as fuel treatment/harvesting plans can be easily integrated and evaluated. It incorporates a series of out-of-the-box planning heuristics that provide benchmarks for comparison. We illustrate their use by applying and evaluating a series of harvesting plans for forest landscapes in Canada. We validated Cell2Fire by using it to predict the growth of both real and hypothetical fires, comparing our predictions with the fire scars produced by a validated fire growth simulator (Prometheus). Cell2Fire is implemented as an open-source project that exploits parallelism to efficiently support the modeling of fire growth across large spatial and temporal scales. Our experiments indicate that Cell2Fire is able to efficiently simulate wildfires (up to 30x faster) under different conditions with similar accuracy as state-of-the-art simulators (above 90% of accuracy). We demonstrate its effectiveness as part of a harvest planning optimization framework, identifying relevant metrics to capture and actions to mitigate the impact of wildfire uncertainty.
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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) | 0.000 | 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 teacher head, 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".