Ignition probability of fine dead surface fuels of native Patagonian forests or Argentina
Bibliographic record
Abstract
Aim of study: The Canadian Forest Fire Weather Index (FWI) is being implemented all over the world. This index is being adapted to the Argentinean ecosystems since the year 2000. With the objective of calibrating the Fine Fuel Moisture Code (FFMC) of the FWI system to Patagonian forests, we studied the relationship between ignition probability and fine dead surface fuel moisture content (MC) as an indicator of potential fire ignition.Area of study: The study area is located in northwestern Patagonia, Argentina, and comprised two main forest types (cypress and ñire) grown under a Mediterranean climate, with a dry summer and precipitations during winter and autumn (~500-800 mm per year).Material and Methods: We conducted lab ignition tests fires to determine the threshold of fine dead fuel ignition at different MC levels. Moisture content of dead fine surface fuels in the field was measured every 10-15 days from November to March for three seasons. We calculated the FFMC during these seasons and correlated it with the measured MC by applying a logistic regression model. We combined the results of the ignition tests and of the regressions to suggest FFMC categories for estimating fire danger in Patagonian forests.Main results: The ignition threshold occurred at MC values of 21.5 and 25.0% for cypress and ñire sites, respectively. The MC measured varied from 7.3 to 129.6%, and the calculated FFMC varied between 13.4 and 92.6. Highly significant regressions resulted when FFMC was related to MC. The ignition threshold corresponded to a FFMC=85. We proposed to divide the FFMC scale in three fire danger categories: Low (FFMC≤85), High (85<FFMC≤89) and Extreme (FFMC>89).Research highlights: Our results provide a useful tool for predicting fire danger in these ecosystems, and are a contribution to the development of the Argentinean Fire Danger Rating and a reference for similar studies in other countries where the FWI is being implemented.Keywords: Austrocedrus chilensi; Nothofagus antarctica; wildfire; fire behavior; fuel moisture; fire weather index.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 teacher head, 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".