Algorithm-centered Approach to Improve Track Performance Monitoring with Rail Profile Data
Bibliographic record
Abstract
This research develops an algorithm using computer programming based on the needs identified by a thorough literature review of current practice in the rail maintenance industry. The algorithm automates a process that estimates the lateral position of wheel-rail contact and corresponding rail profile radii along rail segments. The algorithm uses measured rail profile data as an input and applies rigid contact theory to model contact between a linear wheel profile and the rail profile. It further computes the lateral contact position and contact radius in an integrated development environment which enables it to provide the results in graphical or numerical form on a profile-by-profile basis as well as summary statistics for each rail segment. This process produces expected results when subjected to validation tests. The validation process examines the rationality and tenability of the algorithm output against a series of expected results using rail profile information from selected segments of a closed loop, captive fleet, North American rail transit property. The algorithm output generally agrees with expected results. This paper details the algorithmic process with two selected scenarios as case studies for validation.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".