Fire potential rating for wildland fuelbeds using the Fuel Characteristic Classification SystemThis article is one of a selection of papers published in the Special Forum on the Fuel Characteristic Classification System.
Bibliographic record
Abstract
The Fuel Characteristic Classification System (FCCS) is a systematic catalog of inherent physical properties of wildland fuelbeds that allows land managers, policy makers, and scientists to build and calculate fuel characteristics with complete or incomplete information. The FCCS is equipped with a set of equations to calculate the potential of any real-world or simulated fuelbed to spread fire across the surface and in the crowns, and consume fuels. FCCS fire potentials are a set of relative values that rate the intrinsic physical capacity of a wildland fuelbed to release energy and to spread, crown, consume, and smolder under known or benchmark weather and fuel moisture conditions. The FCCS reports eight component fire potentials for every fuelbed, arranged in three categories: surface fire behaviour (reaction intensity, spread rate, and flame length), crown fire potential (torching and active crown fire), and available fuel potential (flaming, smouldering, and residual smouldering). FCCS fire potentials may be used to classify or compare fuelbeds that differ because of location, structure, passage of time, or management action, based on expected fire behavior or effect outcomes. As a classification tool, they are offered as an objective alternative to categorizing bulk properties of fuelbeds or stylized model inputs.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".