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Record W4391231043 · doi:10.1155/2024/9838979

Retracted: Research on Urban Traffic Industrial Management under Big Data: Taking Traffic Congestion as an Example

2024· article· en· W4391231043 on OpenAlexvenueno aff

Post-publication record

OpenAlex flags this work as retracted, but it carries no matching Retraction Watch record in this frame.

Bibliographic record

VenueJournal of Advanced Transportation · 2024
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicE-commerce and Technology Innovations
Canadian institutionsnot available
Fundersnot available
KeywordsTraffic congestionTransport engineeringTraffic congestion reconstruction with Kerner's three-phase theoryTraffic optimizationBig dataComputer scienceFloating car dataOperations researchEngineeringData mining

Abstract

fetched live from OpenAlex

OpenAlex records an abstract for this work, but it could not be fetched just now.

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)0.012
metaresearch head score (Gemma)0.114
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: none
Teacher disagreement score0.970
Threshold uncertainty score0.138

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.114
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0050.007
Science and technology studies0.0090.007
Scholarly communication0.0160.015
Open science0.0080.009
Research integrity0.0300.030
Insufficient payload (model declined to judge)0.0240.018

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 head0.229
GPT teacher head0.365
Teacher spread0.136 · 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.

Study designNot applicable
Domainnot available
GenreEditorial

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
Published2024
Admission routes1
Has abstractyes

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