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Record W3081609783 · doi:10.1139/cjce-2019-0828

Consistency of interchange outer connection ramps

2020· article· en· W3081609783 on OpenAlexvenueno aff
Hashem R. Al‐Masaeid, Tareq M. Magsi, Hatem Almasaeid

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2020
Typearticle
Languageen
FieldEngineering
TopicTraffic and Road Safety
Canadian institutionsnot available
Fundersnot available
KeywordsGeometric designRADIUSDeflection angleOperating speedDeflection (physics)Consistency (knowledge bases)Reduction (mathematics)MathematicsDesign speedEngineeringGeometrySimulationComputer scienceTransport engineeringPhysicsOptics

Abstract

fetched live from OpenAlex

Interchange ramps need a proper and consistent geometric design to avoid possible traffic accidents. The objective of this study is to develop guidelines for consistent design of outer connection ramps. As such, 47 ramps were selected from 18 different interchanges in Jordan. Free-flow speed measurements, for different vehicle classes, were taken along ramps and at predefined points. Also, traffic accidents, geometric variables, and traffic volumes were obtained. For circular ramps, the analysis indicated that the operating speed reduction is strongly affected by radius and deflection angle of the curve. The radius of the first curve had the greatest impact on speed reduction on curve–straight–curve ramps. For reverse-curve ramps, the ratio of the radii should be 6:4:9 to achieve a good consistent design for cars, provided that the radius of the first curve exceeds 110 m. Further analysis indicated that speed reduction, geometric variables, and traffic volume influenced the occurrence of accidents.

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)0.004
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.999
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.002

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 head0.015
GPT teacher head0.172
Teacher spread0.157 · 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 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

Citations3
Published2020
Admission routes1
Has abstractyes

Explore more

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