Optimizing the re-profiling policy regarding metropolitan train wheels based on a semi-Markov decision process
Why this work is in the frame
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Bibliographic record
Abstract
In this article, we present a maintenance model for metropolitan train wheels subjected to diameter or flange thickness overruns that includes condition monitoring with periodic inspection. We present a dynamic ([Formula: see text], [Formula: see text]) policy based on condition monitoring information, where [Formula: see text] is the wheel flange thickness threshold that triggers preventive re-profiling and [Formula: see text] is the recovery value for the wheel flange thickness after preventive re-profiling. The problem is modelled as a semi-Markov decision process that considers wear in terms of the diameter and flange thickness simultaneously. The problem is formulated in a two-dimensional state space; this space is defined as a combination of the diameter state and the flange thickness state. The model also considers imperfect wheel maintenance. The model’s objective is to minimize the maintenance cost per unit time that is expected in the long run. We apply a policy-iteration algorithm as the computational approach to determine the optimal re-profiling policy and use an example to demonstrate the method’s effectiveness.
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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 | 0.003 | 0.006 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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