Fuel Moisture Model Calibration under Natural Hazard Conditions: A Case Study in Andean Cypress-Coihue Mixed Forests, Northwestern Patagonia
Bibliographic record
Abstract
Recent decades have seen a marked intensification of wildfire regimes worldwide, posing increasing risks to temperate forests. In northwestern Patagonia, Argentina, these changes threaten biodiversity-rich Andean foothill forests dominated by Austrocedrus chilensis and Nothofagus dombeyi. Yet, most operational fuel moisture models, such as those embedded in the Canadian Forest Fire Danger Rating System, remain calibrated for Northern Hemisphere conditions and may not accurately reflect the microclimatic variability of Patagonian mountain forests. This study develops and validates a site-specific calibration of 10-hour fuel moisture models under natural hazard conditions in the El Manso Valley, Río Negro Province. Field-based gravimetric measurements were combined with automated reference-stick data to examine diurnal dynamics of fuel moisture content (FMC) across contrasting canopy types. Linear calibration models were established to quantify relationships between stand-level FMC, local relative humidity, and standardized reference-stick readings. Results demonstrate pronounced species-specific differences: in the open Austrocedrus stand, FMC showed moderate correlation with relative humidity (R² = 0.330), indicating short- term atmospheric coupling. In contrast, Nothofagus fuels exhibited almost invariant moisture conditions (R² ≈ 0.003) due to canopy buffering and reduced air exchange. Standard models such as the Fine Fuel Moisture Code (FFMC) and reference-stick calibrations consistently overpredicted drying rates under shaded and mesic conditions. These findings highlight the need for regionally adapted calibration schemes that incorporate canopy structure and microclimatic controls. The study provides an empirical basis for improving the accuracy of fire danger rating systems in southern temperate forests and contributes to a broader understanding of fuel–climate interactions under increasing wildfire risk.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".