Risk/Cost-based Algorithm for the Routing of Dangerous Goods
Bibliographic record
Abstract
This study develops a risk/cost-based dangerous goods routing algorithm. The algorithm focuses on mitigating the risks associated with the transportation of dangerous goods (DG) via route selection. The algorithm was applied to a large-scale transportation network representing the Metro Vancouver area. The network is represented spatially in a GIS database along with a realtime dispersion plume simulating a specific chemical release under local weather conditions. GIS facilitates the comparison between the various criteria by overlaying transportation networks characteristics on other spatially referenced data, such as population demographics or meteorological data. The algorithm and general methodology is used for the routing of dangerous goods on-demand, serving individual shipments in a permitting environment. The uniqueness of the proposed approach is in the normalization of risks and operating costs such that a costbased DG routing optimization is achieved. Furthermore, the practicality of the algorithm is demonstrated by developing a computer application using Canadian and B.C. datasets.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".