Examining Multiple Dangerous Goods Routing Criteria Within GIS-Based Framework in British Columbia, Canada
Bibliographic record
Abstract
This study identifies and formulates some of the most common dangerous goods (DG) routing criteria and considers two methods for determining the impact zone (IZ) of a DG incident. To make large-scale implementation possible, readily available datasets that are unique to the province of British Columbia were incorporated into a GIS environment to determine the optimal route. Since routing criteria attempt to characterize risk based on different objectives, which might be conflicting, the tradeoffs among different routing criteria are examined. Moreover, two methods to create the impact zones were considered (i) the emergency response guidelines (ERG) isolation and protection action distances; and (ii) a plume dispersion model to effectively incorporate climate conditions, release quantities, DG types, and topography into modeling the release, explosion, or dispersion of DG. A case study was used to demonstrate the differences between the routing criteria as well as using different methods to identify the impact zone. In the case study, three alternative routes were considered for transporting a chlorine shipment between an O-D pair. The ERG and plume dispersion methods produced notably different routing results. Also, there were considerable differences in results among the various routing criteria.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".