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Record W3007601616 · doi:10.37367/jpi.v2i1.47

Analysis of the Carrying Capacity of Kranji Traction Substation in the Operation of Soekarno-Hatta Airport Train

2018· article· en· W3007601616 on OpenAlexaff
Catur Wicaksono, Akhwan Akhwan, Ava Rizkinda Putri

Bibliographic record

VenueJurnal Perkeretaapian Indonesia (Indonesian Railway Journal) · 2018
Typearticle
Languageen
FieldEngineering
TopicRailway Systems and Energy Efficiency
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsHeadwayEngineeringCarrying capacityTransformerTraction (geology)Automotive engineeringLoad factorElectrical engineeringVoltageStructural engineeringTransport engineeringMechanical engineering

Abstract

fetched live from OpenAlex

This research was prepared with the aim of carrying out the calculation of the capacity of the traction substation and analyzing the carrying capacity of the Kranji Traction Substation at the operation of the Soekarno-Hatta Airport Train at rush hour in the morning. This research was conducted with data sources in the form of a train circuit arrangement, filling distance between traction substations, headway, double track type, train consumption ratio, and total train weight. The capability parameters of the substation carrying capacity are measured based on the narrowing of the 8.5 minute, 5 minute and 3 minute headway and the blackout of the Cakung substation. The plan load value is obtained based on the ratio of the maximum current load to the headway constriction load. Carrying capacity is measured based on the comparison of planned load values ​​to the existing capacity of substations. The results of the analysis stated the rectifier load was 2626.42 kW and the transformer load was 3533 kVA on all types of parameters. With a rectifier capacity of 43% and a transformer of 51%. An evaluation of the results of the data analysis showed that the Kranji Traction Station was able to supply power for the operation of the Soekarno-Hatta Airport Train in the morning rush hour.

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.

How this classification was reachedexpand

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.518
Threshold uncertainty score0.911

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.015
GPT teacher head0.223
Teacher spread0.208 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2018
Admission routes1
Has abstractyes

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