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Record W654419559

ICTIS 2013 : improving multimodal transportation systems - information, safety, and integration : proceedings of the second International Conference on Transportation Information and Safety : June 29 - July 2, 2013, Wuhan, China

2013· book· en· W654419559 on OpenAlexaboutno aff
Xinping Yan

Bibliographic record

VenueAmerican Society of Civil Engineers eBooks · 2013
Typebook
Languageen
FieldEngineering
TopicTraffic Prediction and Management Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsInformatizationChinaDigitizationTransport engineeringIntelligent transportation systemEngineeringThe InternetInformation technologyTelecommunicationsComputer scienceGeographyWorld Wide Web
DOInot available

Abstract

fetched live from OpenAlex

Proceedings of the Second International Conference on Transportation Information and Safety (ICTIS), held in Wuhan, China, June 29-July 2, 2013. Sponsored and organized by Wuhan University of Technology; Transportation & Development Institute of the American Society of Civil Engineers; China Communications and Transportation Association; and Canadian Society for Civil Engineering. This collection contains more than 330 reviewed papers examining critical issues and opportunities in transportation information and safety that influence the sustained development of China's transportation system. The systematic and multimodal application of information technologies and innovations to transportation safety management plays a crucial role in creating cost-effective, environmentally sound, and socially equitable transportation networks. Topics include: intelligent vehicles and vehicle safety technology, transportation safety and human factor engineering, traffic monitoring technology, traffic and transportation information processing, Internet of Things and transportation safety, navigation digitization and maritime informatization, shipping and ocean engineering safety, rail and air safety. Practitioners, researchers, students, and policymakers around the world will find this collection to be a great source of information on current practices 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.

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)0.001
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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.027
Threshold uncertainty score0.091

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0270.006

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 head0.003
GPT teacher head0.171
Teacher spread0.167 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2013
Admission routes1
Has abstractyes

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Same venueAmerican Society of Civil Engineers eBooksSame topicTraffic Prediction and Management TechniquesFrench-language works237,207