Bibliographic record
Abstract
Bradken has been asked by its clients to investigate wheel RCF (Rolling Contact Fatigue) occurring in standard gauge coal rollingstock. In particular high impact wheel occurrence from rail squat like surface initiated sub surface RCF. In the USA similar RCF issues have been under investigation by the AAR with a considerable amount of publications in the last five years. In the USA wheel RCF issues has been linked to sharper curvatures and profile mismatch. The USA study has drawn a link to asymmetric wheel wear with worn rail profiles exhibiting the shapes of the asymmetric worn wheels. These worn rails then exhibit deteriorating contact stresses with most wheels increasing the likelihood of rolling contact fatigue. Canadian experience associates wheel RCF shelling with the occurrence of metal pickup in tread braking. Bradken and other parties have investigated RCF occurrences and the wheel steel manufacture in Australian coal operations. The higher material hardness has appeared to increase wheel RCF occurrence over wear. Brake shoe metal pickup and slip damage appear closely associated with RCF occurrences. The uni-directional Australian operations develop distinct worn wheel profiles. Worn Profiles include asymmetry and a groove beyond the normal running surface. The wheel profile maintenance in Australia has not been subject to optimisation and is far removed from the international best heavy haul practice.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".