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Record W567901571

Warrants for ITS Devices – Results of Warrants Testing to Date

2008· article· en· W567901571 on OpenAlexaboutno aff
Dean Deeter, Ray Starr, Bill Legg

Bibliographic record

Venue15th World Congress on Intelligent Transport Systems and ITS America's 2008 Annual MeetingITS AmericaERTICOITS JapanTransCore · 2008
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicTransportation Systems and Infrastructure
Canadian institutionsnot available
Fundersnot available
KeywordsSoftware deploymentLiberian dollarRisk analysis (engineering)Operations managementBusinessComputer scienceEngineeringFinance
DOInot available

Abstract

fetched live from OpenAlex

Transportation agencies considering the deployment of technology devices to support either operations or maintenance activities often must perform cost/benefit analyses during the planning and design phase. While the costs of these devices are easily quantified, it is often difficult to assign dollar value equivalents to the benefits of such devices. The ENTERPRISE Pooled Fund Study ‘Warrants Project’ has investigated the concept of warrants to determine whether technology devices (specifically ITS devices) should be considered for deployment at specific locations. This project was a pilot study to explore the feasibility of warrants for technology devices. Six state departments of transportation (DOTs), one Canadian province, and the Dutch DOT have participated in development and testing of these warrants. This paper summarizes the testing results and the concept for ongoing use of the warrants.

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)0.016
metaresearch head score (Gemma)0.061
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 score0.016
Threshold uncertainty score0.084

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.061
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0010.001
Scholarly communication0.0020.003
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.002

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 head0.044
GPT teacher head0.255
Teacher spread0.212 · 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

Citations0
Published2008
Admission routes1
Has abstractyes

Explore more

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