Evaluation of SMOS Data to Provide Prefire Conditions’ Information for Forest Fire Danger Rating System in Canada
Bibliographic record
Abstract
Forest fires in Canada’s boreal forest can cause a great deal of concern for populations, environment and infrastructures. One of the tools developed to predict potential fire activity related to these events is the Canadian Fire Weather Index System (FWI<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><sub>CAN</sub></i>). This study aims to analyse the potential of Soil Moisture and Ocean Salinity (SMOS) satellite products (Soil Moisture (SM), Vegetation Optical Depth (VOD) and Root Zone Soil Moisture (RZSM)) to provide additional information on pre-fire soil and vegetion conditions for forest fire danger rating system. Using Random Forest algorithm, we show that adding SMOS data to the FWI<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><sub>CAN</sub></i> system indices slightly increases the accuracy of the predictions of potential fire activity. The RZSM from SMOS data was the variable that best improved the model performance, whereas the VOD provided no additional information. In the aim to evaluate the method for regions with limited in situ meteorological data, we used FWI system indices calculated from ERA5 available over the globe (FWI<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><sub>ERA5</sub></i>). Taking into account FWIERA5, SMOS data allow to improve substantially the ability to predict forest fire.
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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 | 0.002 | 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 | 0.001 |
| 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".