Use of Recycled Rubber Elements in Track Stabilisation
Bibliographic record
Abstract
This paper introduces two novel methods of using waste materials i.e., steel furnace slag (SFS), coal wash (CW), and rubber crumbs (RC) in rail tracks. One method is to optimize the mixtures of SFS, CW, and RC (SFS+CW+RC matrix) compacted with the standard compaction energy to serve as a subballast material. The other one is to examine the potential usage of CW and RC mixtures (CW+RC matrix) which are compacted under adjusted compaction effort. To investigate the geotechnical properties of these waste mixtures, comprehensive laboratory tests have been conducted. Based on the test results, the stress-strain relationship is studied with special focus on the effect of rubber content on the ductility and energy-absorbing potential of the proposed mixtures. In addition, for the CW+RC matrix, the role of rubber content and compaction effort on the compaction and degradation characteristics of the material is examined.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".