Large-scale fire risk planning for initial attack and fuels: the U.S. state of Idaho
Bibliographic record
Abstract
Public officials charged with managing the risk of wildland fire are looking for ways to apply risk analysis at large scales affecting a wide range of resources, including life and property.The ability to address risk analysis at a large scale is just now emerging with new technologies and analytics in such applications as STARFire.This paper summarizes how the STARFire planning and budgeting system were applied across the state of Idaho, and how it can be used to support state-wide planning using the Idaho State Fire Management Plan initiative as an example.The STARFire planning and budgeting system generated a large-scale risk analysis across the entire state of Idaho for the United States Department of Interior's Bureau of Land Management (BLM).The STARFire analysis is driven by a single performance metric 'return on investment' to make efficient use of scarce funding.We collaborated with officials at the BLM to identify and assemble key spatial input data such as: a full range of values at risk, fire behaviour and fire history, and fire management cost information.This and related information was analysed using the STARFire spatial planning and budgeting system to produce a state-wide risk analysis, an integrated fuels and initial attack analysis and an integrated budget analysis across programs.The analysis demonstrated the ability to assess BLM lands across the state and scale between the state and the associated planning units within the state.This is the first time that such an analysis has been performed at such a large scale (across multiple landscapes) at the program level.The associated planning unit level analysis was validated with BLM officials.Products support the BLM's first state-wide spatial fire management planning initiative
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".