A framework for an on-demand dangerous goods routing support system for the metro Vancouver area
Bibliographic record
Abstract
This paper proposes a framework that integrates existing climate conditions with a Geographical Information System (GIS) to develop an on-demand dangerous goods (DG) routing support system. The framework focuses on mitigating the risks associated with DG transportation via route selection. Evidently, DG routing involves a number of decisions that require the consideration of multiple and sometimes conflicting risks. As a result, the framework includes a number of different routing criteria pertaining to safety, efficiency, security, and cost. The framework was applied to a large-scale transportation network representing the Metro Vancouver area. The network was represented spatially in a GIS database along with a real-time dispersion plume model to simulate a specific chemical release under local weather conditions. The results show that different routing criteria lead to different optimal route choices. The authors also compared route selection based on the Emergency Response Guidebook (ERG) for protection and isolation actions with route selection based on dispersion models. The comparison results show that, when employing the ERG in a small spill scenario, decisionmakers are at risk of exposing a large number of individuals to severe health effects. Vice versa, if the ERG was to be followed in a large spill scenario, many individuals who are not at risk would be unnecessarily evacuated. This translates into increased evacuation costs, and wastes the time and effort of emergency personnel. The study shows that these issues are properly addressed if a dispersion model is used to refine the estimation of the impact zone by including measures that are specific to the shipment.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".