Anatomy of the Las Máquinas wildfire using remote sensing tools
Bibliographic record
Abstract
The Las Máquinas wildfire took place in central Chile in the austral summer season of 2017 has becomes the most severe event in Chilean history, causing loss of life, property and the destruction of native forest, crops, large areas of commercial plantations and biodiverse habitats. Since this event has no precedent in Chilean wildfire history, it was used as an example to carry out a detailed analysis of the conditions before (pre-), during (per-) and after (post-) the fire from a remote sensing perspective. The goal of this work is to develop a framework to carry out detailed analyses of catastrophic fires for forensic and public policy purposes, making use of the advantages posed by Earth Observation satellites, including the simultaneous imaging of large areas with a good spatial resolution, and an ever-increasing temporal resolution, coupled with a sophisticated suite of instruments which allow measuring many parameters simultaneously. This study examines the biophysical, meteorological and physical variables like the evolution of the Normalized Difference Vegetation Index (NDVI), weather conditions, maximum temperature evolution, the Canadian Fire Weather Index, the burned area, the maximum fire radiative power, among others, for the five municipalities that were affected by the fire: Cauquenes, Chanco, Empedrado, Constitución and San Javier, all of which are located in the Maule Region. The results indicate that Las Maquinas wildfire took place under exceptional meteorological conditions. In particular, the conditions before the night when Santa Olga was destroyed were characterized by record values of the FWI, which caused a significant increase in the burned area and overwhelmed any response by the fire brigades.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".