Bibliographic record
Abstract
Most of the current formulae for spiral length use either unbalance, Eu or actual super-elevation, Ea to calculate spiral length, L as applied to light rail, commuter rail, and rapid transit. In reality, a train suffers both Eu and Ea run-off simultaneously. Super-elevation unbalance run-off produces the effect of radial acceleration and actual super-elevation run-off produces the effect of rotation. As a result due to this spiral length design should be based on both Ea and Eu. The current formulae underestimate spiral length which affects comfortable jerk rate, roll criteria, and twist rate. These formulae do not shed any light on how to proportion actual and unbalance superelevation to realize desired results. Consequently from these formulae, desirable values of Ea and Eu cannot be calculated, so it is necessary to re-evaluate the current formulae. It is mathematically established that combined effects of Ea and Eu run-off set a limit of 45mm/sec. A new formula for spiral length comes out to be: L = 0.006(Ea+Eu)V. But this formula does not reflect the effect of proportion of Ea and Eu. This gives the same length whatever be the proportion of Ea and Eu. To overcome this deficiency a second formula is derived as: L=VEa161−Eu. The article analyzed the effect of proportion of Ea and Eu on spiral length. The formulae developed in the paper satisfy both cant gradient and rotation criteria that are currently used. If these formulae are used, these criteria do not require checking. The analysis reveals the basis of cant gradient that is to be used for spiral length design. As extensions of these new formulae are developed for minimum spiral length, minimum length of circular curve, rate of twist, maximum safe speed etc. For Heavy haul operation issues of over-loading and off-loading on the twisted track is also discussed to review cant gradient for spiral length design.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.027 | 0.010 |
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".