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Record W791557178

BRT Projects Roll Along Despite Economic Slump

2009· article· en· W791557178 on OpenAlexaboutno aff
Nicole Schlosser

Bibliographic record

VenueMetrologia · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicTransportation Planning and Optimization
Canadian institutionsnot available
Fundersnot available
KeywordsTransit (satellite)Range (aeronautics)BusinessAgency (philosophy)PeninsulaBus rapid transitAgricultural economicsTransport engineeringPublic transportGeographyEngineeringEconomics
DOInot available

Abstract

fetched live from OpenAlex

The 2009 annual review of bus rapid transit (BRT) projects in North America shows that plans are moving along despite the poor economy. The survey covers 35 projects, 33 in the U.S. and two in Canada. Start dates range from 2009 to 2014. Some new projects have been added since last year’s survey, and two in last year’s survey are defunct or delayed. The newest projects are emerging on the west coast, with nearly half planned in Seattle. One of those, King County Metro Transit’s Rapid-Ride, will include five new lines, for a total cost of $171 million. The midwest has the second highest number among those responding to the survey, with five in the region. Two are in Minnesota. Funding sources range from Federal Transit Administration Small Starts, New Starts, and other federal programs (nearly 70 percent of projects) to state funding, which is supporting 31 percent, and local sales taxes, which are going to 54 percent. (Many projects rely on a combination of these sources.) A total of 119 vehicles are projected to be purchased for BRT implementation, with 54 percent of the projects using clean diesel, followed closely by hybrid-electric. Many transit authorities are using two propulsion methods, so percentages overlap. That represents a slight decrease in hybrid-electric and a slight increase in clean diesel from the previous year. Included in the article are survey results for the top 25 cities with bus rapid transit systems. Included for each city are the project or agency, capital cost, start year, number of BRT vehicles, running way features, station characteristics, styles of vehicles, fare collection methods, type of propulsion, and use of Intelligent Transportation System (ITS) features.

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 score0.110
Threshold uncertainty score0.249

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 head0.023
GPT teacher head0.294
Teacher spread0.270 · 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
Published2009
Admission routes1
Has abstractyes

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