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Record W4281703924 · doi:10.13167/2022.24.1

FASTEST PATH VEHICLE SPEED ANALYSIS AT STANDARD TURBOROUNDABOUTS WITH VARIOUS APPROACH LEG POSITIONS

2022· article· en· W4281703924 on OpenAlexaff
Tamara Džambas, Vesna Dragčević

Bibliographic record

VenueElektronički časopis građevinskog fakulteta Osijek · 2022
Typearticle
Languageen
FieldEngineering
TopicTraffic Prediction and Management Techniques
Canadian institutionsTransport Canada
Fundersnot available
KeywordsRoundaboutScope (computer science)Path (computing)Computer scienceGeometric designSimulationEngineeringMathematicsGeometryTransport engineering

Abstract

fetched live from OpenAlex

In previous studies, a new (improved) turboroundabout design approach based on the rules of the design vehicle movement geometry was proposed, and the optimal design of elements of standard turboroundabouts for various design vehicle scenarios, circulatory roadway radii, and approach leg positions was defined. Within the scope of this research, the applicability of the current Dutch calculation model for fastest-path vehicle speed analyses at standard turboroundabout schemes designed by a previously described procedure was examined. Research results have shown that this Dutch calculation model does not apply to standard turboroundabouts whose approach legs are aligned under various angles and translatory shifted regarding the roundabout geometric center, and therefore, should not be used for speed analyses at this roundabout type until a new calculation model, which corresponds to the real traffic situation, is developed.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.005
GPT teacher head0.182
Teacher spread0.177 · 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 designSimulation or modeling
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

Citations2
Published2022
Admission routes1
Has abstractyes

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Same venueElektronički časopis građevinskog fakulteta OsijekSame topicTraffic Prediction and Management TechniquesFrench-language works237,207