Bibliographic record
Abstract
This is a theoretical work on lateral creepage, εy for both tangent and curved track. From the equation of motion on tangent track, the expression for the maximum lateral velocity is obtained and then followed by the basic definition of lateral creepage to obtain the maximum lateral creepage. For tangent track the maximum lateral creepage is given by: εy=y0RK in which, y 0 is wheel clearance, γ is conicity, r is nominal wheel radius, s is the track width, RK is the Klingel radius. Lateral creepage for a curve is expressed by replacing the Klingel radius with the radius of curve, R as shown below: εy=y0R A lateral slip in the running surface exists because of the wheel’s attack angle, which causes the flange to push against the inside of the railhead; consequently, a lateral slip force will be developed [1]. Clearly, this situation provides valid grounds to find a correlation between lateral creepage and the angle of attack. Exactly this is given below. For curved track: εy=0.069B2R+0.2862y0B in which, B is wheel base, R is radius of curve. This equation is suggested for the lateral creepage on the curve, as it contains both components of the angle of attack that connects all important parameters, such as radius, wheel base and wheel clearance. A threshold creepage value of 0.0045 is suggested. It is shown that a curve with a radius greater than 300 m should not produce wheel squeal noise.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 0.006 |
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".