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Record W344991833

Rail Shuttles: Concepts and Case Studies

2008· article· en· W344991833 on OpenAlexaboutno aff
Herbert S. Levinson

Bibliographic record

VenueTransportation Research Board 87th Annual MeetingTransportation Research Board · 2008
Typearticle
Languageen
FieldEngineering
TopicRailway Systems and Energy Efficiency
Canadian institutionsnot available
Fundersnot available
KeywordsControl reconfigurationInterimService (business)Urban transitTransport engineeringTransit (satellite)Rail transitPublic transportLine (geometry)Transit systemBusinessOperations researchComputer scienceEngineeringGeographyMarketing
DOInot available

Abstract

fetched live from OpenAlex

Rail shuttles are “niche” transit services that result from specific circumstances. They include single track shuttles in outlying areas, part-time or all-time extensions of line haul routes, and connections between major activities or transit lines. The paper describes and discusses more than 20 shuttles found in U.S., Canada, London, and Paris. It indicates why and where they were developed. Shuttles are found mainly in a few very large cities with extensive, complex, and old systems—mainly New York City, London, Paris, and Chicago. They reflect needs to better balance capacity and demand, or a result from service reconfiguration. Modern rapid transit systems with limited branching and extensive feeder bus (or park-and-ride) reduce the need for shuttles. Thus, the future shuttles will be more selectively located; they will address the need to connect to major off-line activities, provide interim service until trunk lines are completed or serve dramatic changes in future ridership.

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)0.002
metaresearch head score (Gemma)0.003
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: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.022
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.004
Science and technology studies0.0030.003
Scholarly communication0.0030.003
Open science0.0010.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0090.001

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 head0.085
GPT teacher head0.384
Teacher spread0.299 · 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
Published2008
Admission routes1
Has abstractyes

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Same venueTransportation Research Board 87th Annual MeetingTransportation Research BoardSame topicRailway Systems and Energy EfficiencyFrench-language works237,207