The verification of wildland–urban interface fire evacuation models
Bibliographic record
Abstract
This paper introduces a protocol for the verification of multi-physics wildfire evacuation models, including a set of tests used to ensure that the conceptual modelling representation of each modelling layer is accurately implemented, as well as the interactions between different modelling layers and sub-models (wildfire spread, pedestrian movement, traffic evacuation, and trigger buffers). This work presents a total of 24 verification tests, including (1) 4 tests related to pedestrians, (2) 15 tests for traffic evacuation, (3) 5 tests concerning the interaction between different modelling layers, along with 5 tests for wildfire spread and trigger buffers. The evacuation tests are organized in accordance with different core components related to evacuation modelling, namely Population, Pre-evacuation, Movement, Route/destination selection, Flow constraints, Events, Wildfire spread and Trigger buffers. A reporting template has also been developed to facilitate the application of the verification testing protocol. An example application of the testing protocol has been performed using an open wildfire evacuation modelling platform called WUI-NITY and its associated trigger buffer model k-PERIL. The verification testing protocol is deemed to improve the credibility of wildfire evacuation model results and stimulate future modelling efforts in this domain. Supplementary Information: The online version contains supplementary material available at 10.1007/s11069-023-05913-2.
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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 | 0.008 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".