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Record W1511485029 · doi:10.1109/vnis.1989.98788

Road information systems; impact of geographic information systems technology to automatic vehicle navigation and guidance

2003· article· en· W1511485029 on OpenAlexaff
Na-Mee Lee, Hassan A. Karimi, Edward J. KRAKIWSKY

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicWeb Applications and Data Management
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsComputer scienceInformation systemService (business)Geographic information systemFleet managementIntelligent transportation systemTransport engineeringSystems engineeringTelecommunicationsEngineeringRemote sensingGeography

Abstract

fetched live from OpenAlex

A prototype road information system being developed at the University is a special-purpose geographic information system that collects, organizes, and disseminates information related to the street and highway networks of a city or region. The basic structure is the road and street network made up of a series of nodes and links, by which attributes from different applications can be referenced. The structure, if properly designed, can serve many applications such as route determination and guidance, commercial and service information provision, dispatching and monitoring of fleet vehicles, and road inventory and traffic management. The current research effort of the prototype concentrates on data modeling, spatial database management, communications, and real-time applications for the many automatic vehicle location, navigation, and guidance systems presently being 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)0.002
metaresearch head score (Gemma)0.006
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0010.003
Scholarly communication0.0040.006
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.002

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 head0.005
GPT teacher head0.242
Teacher spread0.236 · 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
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

Citations15
Published2003
Admission routes1
Has abstractyes

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