Evaluation of the Canadian Precipitation Analysis (CaPA) to improve forest fire danger rating
Bibliographic record
Abstract
The Fire Weather Index (FWI) System is the basic method of fire danger rating for fire management agencies in Canada. The spatial interpolation of this weather-based system can be difficult in areas where weather stations are sparse. Previous studies have shown that interpolation of precipitation is especially problematic owing to the isolated nature of convective summer storms. Environment and Climate Change Canada (ECCC) has recently developed the Canadian Precipitation Analysis (CaPA), a gridded precipitation product that integrates forecast data, station observations and Doppler radar, to improve precipitation estimates. The objective of the present study was to evaluate whether CaPA is indeed superior to the currently used interpolation method (thin-plate spline), and to assess the impact of the two precipitation estimates on the FWI System for the province of Ontario. Overall, the results showed the CaPA had increased skill in areas of radar coverage. In non-radar areas, where the ECCC station network was much sparser, there were less significant differences between the methods. Precipitation estimates from the CaPA also significantly improved the estimation of the Fine Fuel Moisture Code, Initial Spread Index and FWI in areas of radar coverage, improving estimates of potential fire danger.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".