A conceptual framework for ranking crown fire potential in wildland fuelbedsThis article is one of a selection of papers published in the Special Forum on the Fuel Characteristic Classification System.
Bibliographic record
Abstract
This paper presents a conceptual framework for ranking the crown fire potential of wildland fuelbeds with forest canopies. This approach extends the work by Van Wagner and Rothermel, and introduces several new physical concepts to the modeling of crown fire behaviour derived from the reformulated Rothermel surface fire modeling concepts proposed by Sandberg et al. (this issue). This framework forms the basis for calculating the crown fire potentials of Fuel Characteristic Classification System (FCCS) fuelbeds (Ottmar et al., this issue). Two new crown fire potentials are proposed (i) the torching potential (TP) and (ii) the active crown potential (AP). A systematic comparison of TP and AP against field observations and Crown Fire Initiation and Spread (CFIS) model outputs produced encouraging results, suggesting that the FCCS framework might be a useful tool for fire managers to consider when ranking the potential for crown fires or evaluating the relative behaviour of crown fires in forest canopies.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".