Bibliographic record
Abstract
Jerk, lateral acceleration, and unbalanced superelevation are induced at the point of switch due to the combined effect of the switch angle and speed. Two sets of formula are derived to compute the values of jerk, lateral acceleration, and unbalanced superelevation due to the switch angle and speed separately. A sudden change in the direction of motion by the switch induces jerk which affects passenger comfort. The jerk due to the sudden change in the direction of motion is formulated in this paper by using the cubic spiral formula. It is demonstrated that the jerk at the point of switch has a positive (direct) relation with the speed and switch angle, but has a negative (inverse) relation with the pivot pitch. It is demonstrated that the switch angle should be optimized to reduce the lateral jerk. To capture the effect of the speed, the effective radius at the point of switch is formulated first and then formulas are derived for the jerk, lateral acceleration, and unbalanced superelevation at the point of switch due to speed. Finally, the aforementioned formulas are added to obtain the total values at the point of switch. The formulas are applied on American Railway Engineering and Maintenance-of-Way Association (AREMA) turnouts and validated by computer simulation using Vampire. These formulas would be helpful for the practicing designers and engineers.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".