Research on Coordinated Development of a Railway Freight Collection and Distribution System Based on an “Entropy-TOPSIS Coupling Development Degree Model” Integrated with Machine Learning
Bibliographic record
Abstract
In recent years, with the gradual networking of high-speed railways in China, the existing railway transportation capacity has been released. In order to improve transportation capacity, railway freight transportation enterprises companies have gradually shifted the transportation of goods from dedicated freight lines to passenger-cargo lines. In terms of the organization form of collection and distribution, China has a complete research system for heavy-haul railway collection and distribution, but the research on the integration of collection and distribution of the ordinary-speed railway freight has not been completed. This paper combines the theories of the integration of collection and distribution theory, coordination theory, and coupling theory and incorporates the machine learning fuzzy mathematics to construct an “Entropy-TOPSIS Coupling Development Degree Model” for dynamic intelligent quantitative analysis of the synergy of railway freight collection and distribution systems. Finally, we take the Tongchuan Depot of “China Railway Xi’an Group Co., Ltd.” as a research object to construct a target system and use the intelligent information acquisition system to collect basic data. The analysis results show that through the coordinated control of the freight collection and distribution system, the coordination between the subsystems of the integrated freight collection and distribution system is increased by 5.94%, which verifies the feasibility of the model in the quantitative improvement of the integration of collection and distribution system. It provides a new method for the research of integrated development of railway freight collection and distribution.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".