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Record W2383976678

Space Needed for Left-Turns on Arterial Roadways

2011· article· en· W2383976678 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicEvaluation Methods in Various Fields
Canadian institutionsCAE (Canada)
Fundersnot available
KeywordsChinaTransport engineeringSpace (punctuation)Point (geometry)Control (management)Left behindComputer scienceEngineeringGeographyMathematicsMedicineGeometry
DOInot available

Abstract

fetched live from OpenAlex

Emphasizing through traffic and neglecting left-turn traffic is mainly responsible for unreasonable road design and poor connections between arterial and local roadways.Based on a large number of cases from the typical urban roadways in the U.S.and Japan,this paper summarizes that roadway design in these countries do consider left/right turn traffic needs.The pa-per presents a methodology of calculating space needed for left-turns on arterial roadways,including two parts of space at in-tersections and roadway segments.By recognizing increasing left-turn traffic in China,the authors point out that the left-turn space should be emphasized in road design and traffic organiza-tion.Finally,through analyzing the disadvantages of current traf-fic control for left-turn traffic in China and the difference from other countries,the paper proposes traffic control for left-turns with double cross-section roadway design and odd number of cross-section design in China.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.658
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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0340.001

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 head0.098
GPT teacher head0.324
Teacher spread0.226 · 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

Quick stats

Citations0
Published2011
Admission routes1
Has abstractyes

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