Repeatable Procedure for Determining a Representative Average Rail Profile
Bibliographic record
Abstract
The use of rail profile measurements for the planning and specification of rail-grinding activities normally involves comparing the existing and desired rail profiles within a rail segment. In current practice, a somewhat subjective approach is used to select a measured profile—usually located near the midpoint of the segment—that represents the profiles throughout the rail segment. Industry-standard rail profile data were used to develop an automated procedure for calculating a representative average (mean) rail profile for a rail segment. The procedure was verified by comparing the calculated average with an expected profile. Then, it was validated by comparing the calculated average profiles of 42 in-service rail segments (10 tangents and 32 curved segments) with the Corresponding median rail profiles for each segment, chosen subjectively. Validation results indicated that the coordinates comprising the mean and median profiles differed by less than 1%, on average. Agreement was stronger for tangent rail segments than for curved rail segments, as expected. Therefore, validation demonstrated that the procedure yields results comparable with current practice while it improves the objectivity and repeatability of the decisions that support rail-grinding activities. The procedure also offers the opportunity for integration with existing software tools to help automate the specification of grinding activities that minimize metal removal and prolong rail life.
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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 | 0.009 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".