Progress Report 1: Resilience and Adaptation to Climatic Extreme Wildfires (RACE Wildfires)
Bibliographic record
Abstract
This is the first 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 first phase, the research performed included two main tasks: 1) the development of a sub-model for the representation of the impact of reduced visibility conditions on driving speed and 2) the development of a conceptual model for the study of the impact of the pandemic on shelter availability and destination choice. An experimental dataset collected in a virtual reality environment has been used to develop a sub-model for macroscopic traffic models considering the impact of reduced visibility conditions on driving speed. An application of a calibrated traffic model considering the impact of smoke has been performed using the WUI-NITY platform, an open multi-physics platform which includes wildfire spread, pedestrian response and traffic modelling. Verification testing has been performed as well. A conceptual framework for the development of a destination choice model to be applied in wildfire scenarios has also been developed.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".