Evaluasi Kinerja Dalam Pelayanan Kereta Rel Listrik Eksekutif Jabodetabek
Bibliographic record
Abstract
The Problem that need to be handle at the moment is an unbalance demand for the executive class inrailwm; services accross Jabodetabek during rush hour traffic with the utilities capacity available,which is make low qualihJ factor of service . Conneccted to the problems above, various efforts ha-uebeen made btJ Kereta Api ( Persero), but it was not yet maximum refraction. So this str.A.dy wasconducted to determine the service performance of KRL, Executive Jabodetabek Using Seater Plotanalysis approach, btj set forward the analysis that need attention and imprmiement is the accuracyof trafel time, waiting time, standard rooms and llounge facilities, standards and facilities up dawninformation systems in train, there are variables which are not problematic, but needs tobe preservedis the level of safehJ, standard lighting, and expousure, air circulation, but there are other things thatneed attention is the level of travel speed trains, ticket purchase service information system at thestation, accuraciJ of travel time, waiting time of arrival, facilities for passenger trains up and dawn.Keyword : Level of service, KRL Jabodetabek, Eksekutif Class.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".