MétaCan
Menu
Back to cohort
Record W1590052933 · doi:10.17226/22809

A Guide for Implementing Bus on Shoulder (BOS) Systems

2012· book· en· W1590052933 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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueTransportation Research Board eBooks · 2012
Typebook
Languageen
FieldEngineering
TopicTraffic control and management
Canadian institutionsnot available
Fundersnot available
KeywordsComputer science

Abstract

fetched live from OpenAlex

This report provides guidelines for the planning, design, and implementation of Bus On Shoulder (BOS) operations along urban freeways and major arterials. Making BOS a reality requires a closely coordinated partnership effort involving transit agencies, state departments of transportation (DOTs), metropolitan planning organizations (MPOs), enforcement agencies, and the Federal Highway Administration (FHWA). The report should be useful as a decision-making guide to assist transit operators, state DOTs, MPOs, and other stakeholders in assessing the feasibility of the BOS concept, developing safe and effective BOS plans, implementing initial BOS operations, and maintaining or expanding ongoing BOS operations. This guide includes seven case studies in the following cities: Minneapolis-St. Paul, Minnesota; Miami-Dade, Florida; San Diego, California; Old Bridge, New Jersey; Ottawa, Ontario, Canada; Columbus, Ohio; and Atlanta, Georgia.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.274
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
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 head0.066
GPT teacher head0.330
Teacher spread0.264 · 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