TRACK QUALITY INDEX AS TRACK QUALITY ASSESSMENT INDICATOR
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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