Improving the prediction of wildfire potential in boreal Alaska with satellite imaging radar
Bibliographic record
Abstract
ABSTRACT Alaska currently relies on the Canadian Fire Weather Index (FWI) System for the assessment of the potential for wildfire and although it provides invaluable information it is designed as a single system that does not account for the varied fuel types and drying conditions (day length, permafrost, decomposition rate, and soil type) that occur across the North American boreal forest. The FWI System is completely weather-based using noontime measurements of precipitation, relative humidity, temperature and wind speed. The most common problem observed with the FWI system is in the initialisation and need for calibration of one of the moisture codes that make up the FWI system, the Drought Code (DC), which is representative of the deeper organic soil layers and has a 53 day lag period. SAR data represent an innovative tool to improve the current weather-based fire danger system of interior Alaska by initialising the spring values of DC, calibrating the codes throughout the season and providing additional point-source data. Using radar backscatter values from several recently burned boreal forests, an algorithm was developed that related backscatter to DC. The authors then demonstrated the application and validation of this algorithm at independent test sites with good correlation toin situsoil moisture and rainfall variations.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".