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Record W2035697315 · doi:10.1139/l08-098

Study on the effect of two different projects on commuter rail line capacity

2009· article· en· W2035697315 on OpenAlexvenueno aff
Zübeyde Öztürk, Turgut Öztürk

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2009
Typearticle
Languageen
FieldEngineering
TopicRailway Engineering and Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsTrainLine (geometry)Bridge (graph theory)Transport engineeringRailway lineEngineeringComputer scienceGeography

Abstract

fetched live from OpenAlex

In this study, the effect of two different projects on the capacity of a commuter rail line is explored. The study reviews line capacities in the case of a mixed operating system or in the case of operation of commuter trains only. Commuter rail lines operated in two regions of Istanbul are taken as an example. These quite inefficient lines will be modernized and joined and integrated. Two projects exist for updating and integrating these lines, one of which is the Marmaray project. Under the ongoing Marmaray project planned for completion in 2010, the Bosporus will be crossed through a tube that will join the commuter rail lines in the neighboring continents. Another planning alternative, in addition to the Marmaray project, is to build a suspended bridge to serve railway traffic and to allocate this line to high-speed trains and other long-distance trains. This will enable operation of commuter trains and high-speed trains on separate lines. Commuter rail line capacities for both modes of operation are calculated separately using line data and the results are reviewed.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.025
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.995
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.025
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.013
GPT teacher head0.201
Teacher spread0.189 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2009
Admission routes1
Has abstractyes

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