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Record W616709343

Fast Times at Edmonton Transit System

2006· article· en· W616709343 on OpenAlexaboutno aff
Steve Hirano

Bibliographic record

VenueMetrologia · 2006
Typearticle
Languageen
FieldSocial Sciences
TopicTransportation Planning and Optimization
Canadian institutionsnot available
Fundersnot available
KeywordsBus rapid transitTransport engineeringAttritionTransit (satellite)Transit systemRail transitService (business)BusinessMileSecurity systemLight rail transitPublic transportTelecommunicationsComputer scienceEngineeringComputer securityGeographyMarketing
DOInot available

Abstract

fetched live from OpenAlex

This article describes some of the difficulties presented to the Edmonton Transit System (ETS) in Alberta, Canada. Some of these challenges include rapid population growth, aging infrastructure, and high employee attrition rates. The system's 800 buses have an average age of 12.5 years, with the city's 37 rail cars averaging 25.5 years of age. The 7.6 mile long light rail system is perhaps in need of the most attention, with expansion and infrastructure needs. The cars will be refurbished within three to four years of the writing of the article at a cost of $26 million. In addition to existing bus service, the ETS general manager explains the development of a bus rapid transit (BRT). The article also describes improvements that have been made to transportation security in the ETS, with 34 special constables to enhance security. Also described briefly is an innovative program to encourage bus riders to read while in transit.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.441
Threshold uncertainty score0.888

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.2420.040

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 head0.009
GPT teacher head0.235
Teacher spread0.227 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2006
Admission routes1
Has abstractyes

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