Bibliographic record
Abstract
The aim of the paper is to classify restraining rail, discuss the advantages and disadvantages of each type of restraining rail, derive the formula to determine the flangeway gap, and finally, to suggest a type of restraining rail for use. Three types of restraining rail are classified as: 1. Active restraining rail: Defined as the restraining rail that reduces the angle of attack (AOA) by more than 50%. 2. Semi-active restraining rail: Defined as the restraining rail that reduces the AOA by 50% or less, preferably between 40% ∼ 50%. 3. Passive restraining rail: Defined as the restraining rail that does not reduce the AOA. In other words, it plays a passive role in steering the wheel. A design procedure is established to estimate the flangeway gap. The advantages and disadvantages of each type of restraining rail are discussed from design, maintenance, and functional points of view. The issue of optimization of the rail/wheel profile is also discussed in context with the presence of restraining rail. One component of the flangeway gap is the space required for the angularity of the wheel. 2D CAD drawings are not efficient for this purpose, as the drawings cannot consider the AOA and the height of restraining rail on top of the rail level. In this context, Nytram plot is a solution; however, the plot needs a number of sectional drawings at gauge point level, rail level, and the top-of-restraining-rail level from the 3D drawing. A mathematical model that counts both the AOA and the height of restraining rail from the top of the rail level is developed here to capture the essence of the Nytram plot, and thereby to assess the space required for the angularity of the wheel. Finally, a semi-active restraining rail, with a formula for flangeway gap, is suggested for use. Being less elaborate and less time consuming, the formula is easier and quicker than the Nytram plot to estimate flangeway gap. Moreover, one can quickly assess the effect of wheel size, the height of the restraining rail from the top of rail, and the radius of the curve on the flangeway gap.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".