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Record W2011748091 · doi:10.3141/2006-04

Success and Challenges in Modernizing Streetcar Systems

2007· article· en· W2011748091 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueTransportation Research Record Journal of the Transportation Research Board · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicTransportation Planning and Optimization
Canadian institutionsUniversity of Toronto
FundersMonash University
KeywordsTransport engineeringTransit systemLight rail transitPublic transportLight railBusinessQuality (philosophy)TelecommunicationsTransit (satellite)Public administrationEngineeringPolitical science

Abstract

fetched live from OpenAlex

On-street running in mixed traffic has been identified as the least desirable right-of-way for light rail and tram systems. While most cities in the developed world have withdrawn streetcar systems, substantial networks have been retained in Melbourne, Australia, and Toronto, Canada. Although some commentators have seen the retention of these systems as visionary, there are substantial challenges to be faced in addressing conflicts between streetcars and rising road traffic. Poor running speeds, unreliability, safety, and difficulties in providing universal access are significant issues for modern streetcar systems. Experiences are described in regard to planning and operating the Melbourne and Toronto streetcar systems. The types of challenges being faced in providing services are contrasted. Programs to address the challenge of creating modern high-quality transit systems out of streetcars are compared. Finally, success strategies in modernizing streetcar systems are identified.

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)0.015
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 score0.372
Threshold uncertainty score0.969

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0150.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.002
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 head0.216
GPT teacher head0.424
Teacher spread0.208 · 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