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Record W2057755366 · doi:10.1061/jhtrcq.0000063

Design and Application of 3D Laser Profile Data Acquisition System

2011· article· en· W2057755366 on OpenAlexaff
Zhishan Wu, Liufu Hu, Xiaobing Li, Wensheng Wu

Bibliographic record

VenueJournal of Highway and Transportation Research and Development (English Edition) · 2011
Typearticle
Languageen
FieldEarth and Planetary Sciences
Topic3D Surveying and Cultural Heritage
Canadian institutionsMinistry of Transportation of Ontario
Fundersnot available
KeywordsData acquisitionLaserSoftwareRealization (probability)Computer scienceMeasure (data warehouse)Laser scanningRoad surfaceScale (ratio)Point cloudSimulationEngineeringComputer visionOpticsData miningMathematics

Abstract

fetched live from OpenAlex

In order to measure the trivial road deformation by the full—scale accelerated load testing, a theory to acquire 3D laser profile data with a single—point laser (2 μm) was proposed to design and develop a measuring instrument. The design and realization of the overall structure and software were discussed in detail. Moreover, the acquisition of 3D laser profile data and the method of graphic reconstruction as well as the measurement error of the instrument were investigated. Thus, 3D information of road surface was obtained by using this measuring instrument with an accuracy of ±0.5 mm. The result indicates that the road indexes such as rut, structural depth and potholes can be acquired through this 3D laser profile data acquisition system with high efficiency and accuracy.

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 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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.214
Threshold uncertainty score0.214

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.069
GPT teacher head0.265
Teacher spread0.196 · 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 teacher head, 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

Citations1
Published2011
Admission routes1
Has abstractyes

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