TRACK QUALITY INDEX AS TRACK QUALITY ASSESSMENT INDICATOR
Bibliographic record
Abstract
Track Quality Index (TQI) is used in order to evaluate track quality. In this paper, TQI application for Indonesian Railway (IR) has been reviewed and various methods to evaluate track quality have been presented. IR has been used TQI-Geometry in infrastructure maintenance works and accident investigation. UK SD Index, Netherlands Q Index, USA TRI, FRA TGI, Austrian TGI, Canadian TQI, SNCF’s MDI, Chinese TQI, Polandia J Coefficient, Indian TGI, and European Standard are some methods to evaluate track quality. However, their results rely only on a limited number of parameters and aspects of track deterioration. Those methods cannot provide a thorough indication of all influencing parameters and their role in track degradation. The author suggests that main track degradation should have 4 aspects: Track Super-Structural; Track Sub-Structural; Track Geometrical; Traffic, and furthermore, a new TQI should be developed by combining 3 index investigations: Track Irregularity, Track Settlement, and Track Geometry.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".