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Record W640156899

Expansion Fuels: GO Transit’s Future

2014· article· en· W640156899 on OpenAlexaboutno aff
Mischa Wanek-Libman

Bibliographic record

VenueRailway track and structures · 2014
Typearticle
Languageen
FieldEngineering
TopicUnderground infrastructure and sustainability
Canadian institutionsnot available
Fundersnot available
KeywordsTransport engineeringTrainEngineeringTrack (disk drive)Work (physics)Transit (satellite)Bridge (graph theory)RelocationService (business)Grading (engineering)Public transportCivil engineeringComputer scienceBusinessGeography
DOInot available

Abstract

fetched live from OpenAlex

GO Transit is a combined regional network of trains and buses that serves the Greater Toronto and Hamilton Area (GHTA). Train ridership on the network is expected to nearly double by 2020. In order to remain competitive with frequent and reliable service, GO Transit has embarked on the Georgetown South Project (GTS), which is all about capacity. The objective of the GTS Project is to meet the needs of GO Transit's Kitchener, Milton and Barrie rail corridors, while building infrastructure to meet future needs. The GTS Project consists of the widening and modifying of 15 bridges; the construction of one new bridge; seven road-to-rail grade separations; one rail-to-rail grade separation; lowering the rail corridor at three locations; station work and major track and grading construction; civil works; signal installations; and utility relocation. In addition to this work the GTS Project rail expansion will accommodate the new Union Pearson Express that will connect Toronto Pearson International Airport and Union Station. Prior to the start of the project, GO train service in the region was limited only to rush-hour because of the availability of a single track north and west. Once the project is complete, the expanded corridor will accommodate between four and eight total tracks. If the improvements to the corridor are not made, the region will require eight additional highway lanes by 2031 in order to meet the demand.

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)0.000
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.871
Threshold uncertainty score0.835

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 communication0.0000.000
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 head0.004
GPT teacher head0.191
Teacher spread0.187 · 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 designOther design
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
Published2014
Admission routes1
Has abstractyes

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