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Record W3005562718 · doi:10.1155/2020/7645142

Mass Rapid Transit Operation and Maintenance Cost Calculation Model

2020· article· en· W3005562718 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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of Advanced Transportation · 2020
Typearticle
Languageen
FieldEngineering
TopicUrban Transport Systems Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsCapital costKilometerTransit (satellite)Transport engineeringTotal costUnit costTransit systemCapital cityUnit (ring theory)Cost analysisCost estimateOperating costEnvironmental scienceOperations managementEngineeringReliability engineeringBusinessMathematicsPublic transportEconomicsWaste management

Abstract

fetched live from OpenAlex

Mass rapid transit (MRT) is an efficient transportation mode that is urgently needed by a growing city such as Jakarta, Indonesia. However, limited research has attempted to evaluate the system’s current performance through a comprehensive, unit-based calculation of the costs of MRT operation and maintenance. This research aimed to develop a system for calculating and comparing MRT operation and maintenance costs per kilometer per year. The cost model has three components, namely, capital, operation cost, and maintenance cost, which are, respectively, calculated based on their percentage toward total cost. The cost model calculation determined that Jakarta MRT operation and maintenance costs total USD 8.44 million per kilometer per year. This result was compared to other countries’ MRT operations.

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)0.000
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.679
Threshold uncertainty score0.527

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.011
GPT teacher head0.204
Teacher spread0.193 · 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