A Study of Methods for Spatial Interpolation of Fire Weather in the Canadian Prairies
Bibliographic record
Abstract
Thousands of wildfires occur in Canadian forests every year and it is very challenging for fire management agencies to predict weather conditions and fire risk especially in the areas with low weather station density. This thesis compares several existing interpolation models (ordinary kriging, ordinary cokriging, and thin-plate spline smoothing) to the inverse \ndistance weighting which is used by Canadian fire management agencies using weather station data from Manitoba and Saskatchewan on a daily basis. The North American Regional Reanalysis (NARR) data extracted from physical models, which has a strong correlation with weather station data, is integrated into spatial interpolation models. This thesis also \nintegrates elevation into ordinary cokriging and the thin-plate spline smoothing models. Results show that integrating NARR into ordinary cokriging and thin-plate spline smoothing model increases prediction accuracy in areas with low station density and could be potentially \nuseful for Canadian fire management agencies.
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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 | 0.010 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".