Bibliographic record
Abstract
Assessment of a rail break gap is important to ensure safety of rail traffic. An axial rail model available in current textbook yields a single formula in terms of breathing length to compute a rail break gap in a continuously welded rail (CWR) track. Transit Co-operative Research Program (TCRP) suggests three formulas to compute a rail break gap in a CWR track. The longitudinal resistance of a track or rail is a significant input to compute a breathing length which in turn determines a rail break gap. TCRP does not recommend value of longitudinal resistance of a track or rail but implicitly suggests dividing fastener restraint force by fastener spacing to determine longitudinal resistance value in the derivation process of its first and third formula. The theoretical and practical deficiencies of first and third formula are discussed. TCRP second formula, simplified version of the first one, appears to be acceptable provided the longitudinal resistance of a track or rail is not computed by dividing fastener restraint force by fastener spacing. The value of longitudinal resistance obtained by dividing fastener restraint force by fastener spacing is underestimated for a direct fixation track leading to a large rail break gap and is not appropriate for a ballasted track as well; an explanation for underestimation and inappropriateness is given. The track professionals would be benefitted by the clarity given in the paper. A call is made for review of TCRP formulas.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.011 |
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".