Predicting the elevated dead fine fuel moisture content in gorse (Ulex europaeus L.) shrub fuels
Bibliographic record
Abstract
Methods were developed to predict the moisture content of the elevated dead fine fuel layer in gorse ( Ulex europaeus L.) shrub fuels. This layer has been observed to be important for fire development and spread in these fuels. The accuracy of the Fine Fuel Moisture Code (FFMC) of the Canadian Fire Weather Index System to predict the moisture content of this layer was evaluated. An existing model was used to determine the response time and equilibrium moisture content from field data. This response time was incorporated into a bookkeeping model, combining the FFMC and this response time–equilibrium moisture content model. The FFMC poorly predicted the elevated dead fuel moisture content in gorse fuels, and attempts to improve its accuracy through regression modelling were unsuccessful. The response time of the elevated dead fine fuel layer was very fast (38–77 min) and has important implications for fire danger rating. The bookkeeping approach was the most promising method to predict elevated dead fuel moisture content. A limitation was the inability to model fuel-level meteorology. However, this model warrants further validation and extension to other shrub fuels and could be incorporated into existing fire danger rating systems that can utilize hourly weather data.
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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 | 0.000 | 0.001 |
| 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 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".