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Record W2773389852 · doi:10.2749/222137817822208780

Eglinton Crosstown and Evergreen Line LRTs - Structural Design on Mass Transit Projects

2017· article· en· W2773389852 on OpenAlexaffabout
Yuming Ding, Roger Woodhead, Samson Chan

Bibliographic record

VenueReport · 2017
Typearticle
Languageen
FieldEngineering
TopicUnderground infrastructure and sustainability
Canadian institutionsSNC-Lavalin (Canada)
Fundersnot available
KeywordsTransit (satellite)Rapid transitRail transitTransport engineeringTransit systemLine (geometry)Light railLight rail transitUrban transitEngineeringCivil engineeringComputer sciencePublic transport

Abstract

fetched live from OpenAlex

<p>The structural design for urban transit projects has its unique challenges, such as train dynamic loadings and rail-structure interactions. Mass transit systems in an urban environment often involve guideway in tunnels, including bored tunnel and cut and cover tunnels. The tunnel structural design must take into consideration the integration of passenger train system requirements such as dynamic envelope, space for cables, emergency walkways, and train control.</p><p>Using the Eglinton Crosstown and Evergreen Line projects as examples, the structural design challenges and solutions for a light rail system are discussed, and lessons learned from past projects are summarized. The “Crosstown” is a light rail transit line currently under construction in Toronto, Ontario and will run across Eglinton Avenue between Mount Dennis and Kennedy Station. The 19-kilometre corridor includes a 10-kilometre underground portion, 25 stations and stops, and a maintenance and storage facility and operations and control centre.</p><p>The Evergreen Line is an 11-kilometre extension to the existing SkyTrain system in Metro Vancouver which was completed in 2016. It contains elevated guideway, at-grade guideway, bored tunnel and cut and cover tunnels, stations, and a vehicle storage facility.</p>

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.407
Threshold uncertainty score0.648

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 head0.022
GPT teacher head0.261
Teacher spread0.240 · 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

Citations0
Published2017
Admission routes2
Has abstractyes

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