Network-of-Networks Framework for Multimodal Hazmat Transportation Risk Mitigation: Application to Used Nuclear Fuel in Canada
Bibliographic record
Abstract
The transportation of hazardous materials (hazmat) is a complex process that continues to receive considerable attention from practitioners and researchers alike. A multimodal system that integrates road and railway networks is often argued as the optimal solution. Given the unique characteristics of each network, the integration of both networks generates an additional layer of complexity attributed to their interdependency. In this respect, the current study utilizes complex network theory to develop a network-of-networks (NoN) framework for multimodal hazmat transportation. Hazmat transportation routes are informed through various measures, including the shortest distance and least vulnerable routes. The case study considered herein applied the developed NoN framework to used nuclear fuel transportation. The analysis results indicate that optimal transportation routes are different for the shortest path and the least vulnerable analysis. These results are expected to inform future hazmat transportation route planning to incorporate more complex and dynamic network characteristics.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".