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Record W626437776

BRT Projects Grow, Systems Diversify

2006· article· en· W626437776 on OpenAlexaboutno aff
Joey Campbell

Bibliographic record

VenueMetrologia · 2006
Typearticle
Languageen
FieldSocial Sciences
TopicTransportation Planning and Optimization
Canadian institutionsnot available
Fundersnot available
KeywordsBus rapid transitRevenueBusinessCapital expenditureCapital costTransport engineeringOrder (exchange)Agency (philosophy)Plan (archaeology)Service (business)Computer scienceFinanceOperations managementMarketingEconomicsGeographyEngineeringPublic transport
DOInot available

Abstract

fetched live from OpenAlex

This article, part of a special issue focusing on Bus Rapid Transit (BRT), presents a survey of BRT projects as of 2006. The survey covers 35 projects in 25 cities, spanning the U.S., Canada, Mexico and Puerto Rico. The survey reports on the agency that runs the project, the capital cost, year of start, number of vehicles, running way features, station characteristics, vehicle styles, fare collection methods, propulsion and use of intelligent transportation technologies (ITS). The total capital cost is estimated to be $5 billion, with roughly half that spent on projects already operating or projected to start operating in 2006. Additional descriptions include the way BRT is evolving, some common trends and ways to more accurately classify BRT. Capital costs seem to drop as projects approach implementation, perhaps in order to speed the move to revenue service, if only on more limited levels. The total number of BRT vehicles agencies plan to buy in 2006 is 247. Other cities and Canadian provinces which did not respond to the survey but which have BRT planned or underway are also listed.

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 score0.210
Threshold uncertainty score0.999

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 spread0.239 · 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
Published2006
Admission routes1
Has abstractyes

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