Calibrating the Fine Fuel Moisture Code for grass ignition potential in Sumatra, Indonesia
Bibliographic record
Abstract
Grass moisture and ignition studies were conducted in central Sumatra, Indonesia, to develop an indicator of grass ignition potential using the Fine Fuel Moisture Code (FFMC) of the Canadian Forest Fire Weather Index System. Moisture content of live and dead grass was measured at three sites every 6 days over an 8-month period. Grass curing was highly variable but averaged 37–39% and often exceeded 50% from April to mid-August. Grass fuel loads averaged 420–722 g/m2. There was a highly significant decrease in dead grass moisture content with increasing FFMC, decreasing grass height, and decreasing total grass biomass. The FFMC was the most influential factor, explaining 54–61% of the dead grass moisture content variation. Ignition tests were applied to live and dead grass samples with specific moisture contents. The ignition threshold of dead and live grass occurred at 35.4% and 27.8% moisture content, respectively. The dead grass ignition threshold corresponded to FFMC values of 81.0–83.3 at the three study sites. Of historical hot spots in South-east Asia, 86% occurred when the FFMC was ≥78, representing the lower 95% confidence interval of the dead grass ignition threshold. The FFMC was calibrated using experimental results for fire management applications.
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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 | 0.001 | 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".