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Record W2950584742 · doi:10.25104/warlit.v22i1.996

Evaluasi Kinerja Dalam Pelayanan Kereta Rel Listrik Eksekutif Jabodetabek

2010· article· en· W2950584742 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueWarta Penelitian Perhubungan · 2010
Typearticle
Languageen
FieldEngineering
TopicUrban Transport Systems Analysis
Canadian institutionsWiLAN (Canada)
Fundersnot available
KeywordsTicketService (business)Computer scienceTrainClass (philosophy)Transport engineeringComputer networkBusinessEngineering

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.640
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.003
GPT teacher head0.170
Teacher spread0.168 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it