Estimation of dead fuel moisture content from meteorological data in Mediterranean areas. Applications in fire danger assessment
Bibliographic record
Abstract
The estimation of moisture content of dead fuels is a critical variable in fire danger assessment since it is strongly related to fire ignition and fire spread potential. This study evaluates the accuracy of two well-known meteorological moisture codes, the Canadian Fine Fuels Moisture Content and the US 10-h, to estimate fuel moisture content of dead fuels in Mediterranean areas. Cured grasses and litter have been used for this study. The study was conducted in two phases. The former aimed to select the most efficient code, and the latter to produce a spatial representation of that index for operational assessment of fire danger conditions. The first phase required calibration and validation of an estimation model based on regression analysis. Field samples were collected in the Cabañeros National Park (Central Spain) for a six-year period (1998–2003). The estimations were more accurate for litter (r2 between 0.52) than for cured grasslands (r2 0.11). In addition, grasslands showed higher variability in the trends among the study years. The two moisture codes evaluated in this paper offered similar trends, therefore, the 10-h code was selected since it is simpler to compute. The second phase was based on interpolating the required meteorological variables (temperature and relative humidity) to compute the 10-h moisture code. The interpolation was based on European Centre for Medium Range Weather Forecasting (ECMWF) predictions. Finally, a simple method to combine the estimations of dead fuel moisture content with other variables associated to fire danger is presented in this paper. This method estimates the probability of ignition based on the moisture of extinction of each fuel type.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".