Risk-Based Model for Effective Marshalling of Dangerous Goods Railway Cars
Bibliographic record
Abstract
Today, railroad companies transport many varieties of dangerous goods (DG). Train \nderailments, especially those involving DG, can be catastrophic in terms of loss of life \nand environmental damage. In North America, the transportation of DG is governed \nby regulations published by the Canadian and United State's governments. While the \nregulation is important in terms of providing overall guidelines, they do not address the \nproblem of optimally positioning DG cars in terms of their potential for derailment and \nthe associated risks. Currently, most rail yard operations do not consider the potential \neffect of the position of DG cars on the risk of derailment. \nThis research is concerned with the problem of how to place DG cars in a train in the \ntrain assembly process so that the overall derailment risk can be minimized. The approach considers both the probability of railway cars derailing en route by position as well as the time associated with additional operations in the rail yard. This work has resulted in a useful decision support tool for assisting rail yard operation managers to achieve an optimum trade-off between derailment risk and operating costs in assembling trains. The merits of this new car placement model are illustrated through a case study of a real railway corridor that connects Barstow Yard in California to Corwith Yard in Chicago over 2100 miles and involves a range of track features. The case study demonstrates that the proposed risk minimization strategy could be implemented with minimal rail yard operation cost.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".