Progress Report 2 : Resilience and Adaptation to Climatic Extreme Wildfires (RACE Wildfires)
Bibliographic record
Abstract
This is the second progress report of the international project funded by the National Research Council of Canada called Resilience and Adaptation to Climatic Extreme Wildfires (RACE Wildfires). In this second phase, the research performed included two main tasks: 1) developments concerning the modelling of smoke and 2) development of analysis methods concerning validation datasets for wildfire evacuation. Visibility in smoke is a key aspect in terms of safe evacuation in wildfire scenarios. As valid results of evacuation modelling tools would rely on an accurate representation of the impact of smoke on people, physical accuracy is required. Therefore, the rendering of smoke needs to be physically based while still being computationally inexpensive so that it can be run in a multi-physics tool in real-time. This report presents an approach for rendering smoke with a single in-scattering term which allows for smoke and light interaction over multiple wavelengths. In addition, analysis methods concerning validation datasets for wildfire evacuation models are presented and discussed. This includes both traditional regression methods as well as approaches based on machine learning.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.006 |
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".