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Record W2521988037

Design and Implementation of Transit Services: Guidelines for smaller communities

2016· article· en· W2521988037 on OpenAlexaboutno aff
Dave Fletcher, M Lee

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Transport and Accessibility
Canadian institutionsnot available
Fundersnot available
KeywordsTransit (satellite)BusinessService (business)DemographicsEnvironmental planningPublic transportTransportation planningTransport engineeringGeographyMarketingEngineering
DOInot available

Abstract

fetched live from OpenAlex

Transit plays an essential role in improving the social, economic, and environmental conditions of Canada’s cities and communities. While there is greater political attention to serving transit needs in larger urban areas, transit services are increasingly vital to improving the well-being of small communities. There are unique challenges small and rural Canadian communities face in providing transit services, compared to large, more urban areas. The approaches to planning for transit and the range of solutions appropriate for providing transit is broader for small communities compared to larger urban centres. Recognizing these unique conditions, the purpose of these guidelines is to provide guidance to planning and transportation professionals in planning for transit services in small communities. The guidelines were developed to tailor to a wide range of different stages of a transit service provision in the community: starting a new service; expanding an existing service; maintaining a service in potential decline. Recognizing that unique characteristics (e.g. land uses, travel patterns, demographics, economic conditions) of small towns and villages, these developed guidelines are intended to identify the directions and considerations required to planning and implementing a new or improved transit service.

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)0.041
metaresearch head score (Gemma)0.077
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: Methods · Consensus signal: Methods
Teacher disagreement score0.095
Threshold uncertainty score0.219

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0410.077
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0050.004
Science and technology studies0.0050.003
Scholarly communication0.0050.005
Open science0.0070.004
Research integrity0.0060.005
Insufficient payload (model declined to judge)0.0120.010

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 head0.158
GPT teacher head0.406
Teacher spread0.248 · 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
GenreMethods

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
Published2016
Admission routes1
Has abstractyes

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