Are maintenance practices for railroad tracks effective
Bibliographic record
Abstract
The Association of American Railroads wished to determine the effect of a maintenance practice known as grinding on the occurrence of rail fatigue defects and on the subsequent total traffic usage before a track must be replaced. Because a designed experiment was not practical, an analysis of historical data from the Canadian Northern Railroad is presented. In the analysis, certain covariate data are available, specifically the amount of grinding and some physical characteristics of the rail; other important covariate data are not available, however. A model for the number of defects as a function of traffic usage is developed based on a modulated Poisson point process. The model incorporates the effect of the available covariates and a mixture of Dirichlet processes set-up for the scale parameters of the individual rail sections that allows an assessment of the overall effect of the unavailable covariates. The model is then used to determine an optimal replacement period for a whole rail track. The analysis demonstrates that grinding reduces the expected number of defects and increases the optimal replacement interval.
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How this classification was reachedexpand
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".