Cascading Reliability Assessment of International Railway Freight Network Based on Coupled Map Lattices: A Case Study of China Railway Express
Why this work is in the frame
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Bibliographic record
Abstract
The cascading reliability problem of international railway freight network is becoming noticeable due to the limitation of node transportation capacity with increases in the transport volume of the international railway freight trains. We discuss this problem in this study, thereby focusing on the failure process of the international railway freight network. As the first step, we consider three factors of node degree, node betweenness, and edge betweenness based on the complex network theory, and establish the node model using coupled map lattice method. Next, we select three indicators to evaluate the reliability characteristics of the network and evaluate the robustness of the network with the maximum effective graph and the network efficiency. Finally, we apply the model to the China Railway Express freight network and consider two situations: cascading failures and noncascading failures that are corresponding to two strategies: redistributing cargoes and disbanding cargoes. The results show that the cascading reliability of the China Railway Express freight network is not high. The indicators decrease less than 10% under noncascading failure, while more than 40% under cascading failure, so the network is more reliable under noncascading failure. Our research provides a new way to test the cascading reliability of the international railway freight network and provide different strategies for improving reliability.
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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 | 0.001 | 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 it