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 (FWICAN). 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 FWICANsystem 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 (FWIERA5). Taking into account FWIERA5, SMOS data allow to improve substantially the ability to predict forest fire.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".