Development of condition‐based tamping process in railway engineering
Bibliographic record
Abstract
Abstract Ballast, rails and sleepers form a quasi‐elastic track system. When the deformations exceed the elastic limit of the system and the track is no longer lying in its correct position, precautions have to be taken. During a technical track examination several parameters are measured. Should the operational tolerance values of these parameters be exceeded, track maintenance needs to be conducted. Track maintenance includes levelling, lifting, lining and tamping of the track, which is performed by a tamping machine, where the tamping tines penetrate the ballast and compact it beneath the sleeper. For the purpose of this research project, a tamping machine was equipped with a number of strategically positioned sensors in order to perform the in‐situ measurements required to describe the interaction of the tamping tines with the ballast and its compaction beneath the sleeper. With a special emphasis on the energy transferred into the ballast and alteration of ballast stiffness during compaction, conclusions concerning efficiency of the tamping process in different ballast conditions are made and presented.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".