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Record W834880087

A WIN-WIM SITUATION

2004· article· en· W834880087 on OpenAlexaboutno aff
Brian Taylor, R Bushman

Bibliographic record

VenueTraffic Technology International · 2004
Typearticle
Languageen
FieldEngineering
TopicTransport Systems and Technology
Canadian institutionsnot available
Fundersnot available
KeywordsWeigh in motionAxleLicenseOverhead (engineering)EngineeringAutomotive engineeringTransport engineeringComputer scienceReal-time computingElectrical engineeringOperating systemStructural engineering
DOInot available

Abstract

fetched live from OpenAlex

The City of Saskatoon in Canada uses a Virtual Weigh Station to monitor compliance with local size and weight regulations. The equipment includes Weigh-in-Motion (WIM) in the driving lane and quartz axle sensors in the passing lane. The video image capture system includes two cameras, a side fire image camera and an overhead license plate camera. The data recorded includes individual axle, axle group, gross vehicle weights, axle spacing and vehicle speed. The system runs continuously. Details are given of the observations made about commercial vehicle traffic at the site. Overloading was predominantly from local traffic operating during the weekday business hours. The benefits of the WIM system are outlined.

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: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.305
Threshold uncertainty score0.607

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0100.002
Scholarly communication0.0030.002
Open science0.0010.004
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0210.003

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 head0.004
GPT teacher head0.191
Teacher spread0.186 · 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
Published2004
Admission routes1
Has abstractyes

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