Analytical approaches to railroad and rail-truck intermodal transportation of hazardous materials
Bibliographic record
Abstract
Hazardous Materials are potentially harmful to people and environment due to their toxic ingredients. Although a significant portion of dangerous goods transportation is via railroads, prevailing studies on dangerous goods transport focus on highway shipments. We present an analytical framework that incorporates the differentiating features of trains in the assessment of risk. Each railcar is a potential source of release, and hence risk assessment of trains requires representation of multiple release sources in the model. We report on the use of the proposed approach for the risk assessment of the Ultra-train that passes through the city of Montreal everyday. The risk assessment methodology is then used to model the operations of freight trains in a network, wherein freight involves both hazardous and regular cargo. We present an optimization model distinct from the conventional ones, a Memetic Algorithm based solution technique, and a number of scenarios intended to gain numerical and managerial insights into the problem. In an effort to combine the economies of trains and efficiencies of trucks, we deal with rail-truck intermodalism for hazardous and non-hazardous cargo. Two special cases and a general case of rail-truck intermodal transportation models, driven by the element of ' time', are presented.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".