Bibliographic record
Abstract
Rail transport adapts to the requirements of the modern agglomerations and provides solutions that ensure greater speed and capacity, while being environmentally friendly. Alternative means of rail transport as well as train control systems are proposed. The basis of innovative, effective, attractive and, above all, safe railway is a traffic automation, which can be implemented to a varying range and degree. Automation of systems of the train control and railway traffic management is an area that is constantly being scientifically researched and developed. The most technologically advanced control systems, in which the human factor is eliminated, are CBTC systems. This article presents the characteristics and components of one of the CBTC class family solutions used in the world, i.e. Bombardier’s product - CITYFLO 650. On the example of the CITYFLO 650 solution, the analysis of the fully automatic train operation was performed. Each of the stages of the fully automatic train operation was included in this paper. The conducted analysis confirms the legitimacy of using the CBTC technology on very demanding city lines. Bidirectional train-to-wayside data communications and determination of train location to a high degree of precision make it that CBTC systems fulfill the criteria set by large, fast-growing cities and a growing population. Poland, as a country currently looking for solutions that would reduce the transport problems of large cities, is considering such innovative proposals for rail traffic.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".