A repeatable procedure to determine a representative average rail profile
Why this work is in the frame
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Bibliographic record
Abstract
The planning and specification of rail grinding activities using measured rail profiles normally involves a comparison between 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. An automated procedure was developed to calculate a representative average (mean) rail profile for a rail segment using industry-standard rail profile data. The procedure was verified by comparing the calculated average to an expected profile. The procedure was then validated by comparing the calculated average profiles of 42 in-service rail segments (10 tangents and 32 curved segments) to the corresponding subjectively chosen median rail profiles for each segment. Overall, the validation results indicated that the coordinates comprising the mean and median profiles differed by less than one percent on average. As expected, stronger agreement was observed for tangent rail segments compared to curved rail segments. Thus, the validation demonstrated that the procedure produces comparable results to current practice while improving the objectivity and repeatability of the decisions that support rail-grinding activities.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it