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Record W2102755342 · doi:10.1109/im.1997.603880

Road surface inspection using laser scanners adapted for the high precision 3D measurements of large flat surfaces

2002· article· en· W2102755342 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicInfrastructure Maintenance and Monitoring
Canadian institutionsInstitut National d'Optique
Fundersnot available
KeywordsRutRoad surfaceTriangulationLaser scanningLaserTransverse planeMeasure (data warehouse)Point cloud3d scanningComputer scienceOpticsSurface (topology)Computer visionMaterials scienceEngineeringPhysicsStructural engineeringGeometryMathematicsAsphalt

Abstract

fetched live from OpenAlex

In this paper an optical configuration based on autosynchronized laser scanning is proposed for the 3D measurement of road surfaces. The advantages of this technique over classical triangulation methods are exposed. The road inspection system developed at the National Optics Institute (NOI) using this type of laser telemetry is also presented. This system uses two autosynchronized laser scanners in order to obtain transverse 3D and intensity profiles of road surfaces. Also described are simple algorithms which detect and measure rutting and cracking conditions. Results include rut measurements on both real and simulated 3D road profiles and a crack map of an actual pavement section.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.187
Threshold uncertainty score0.380

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.033
GPT teacher head0.244
Teacher spread0.211 · 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

Quick stats

Citations52
Published2002
Admission routes1
Has abstractyes

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