{"id":"W2057755366","doi":"10.1061/jhtrcq.0000063","title":"Design and Application of 3D Laser Profile Data Acquisition System","year":2011,"lang":"en","type":"article","venue":"Journal of Highway and Transportation Research and Development (English Edition)","topic":"3D Surveying and Cultural Heritage","field":"Earth and Planetary Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministry of Transportation of Ontario","funders":"","keywords":"Data acquisition; Laser; Software; Realization (probability); Computer science; Measure (data warehouse); Laser scanning; Road surface; Scale (ratio); Point cloud; Simulation; Engineering; Computer vision; Optics; Data mining; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000916123,0.0005627171,0.000700074,0.001820227,0.0005238188,0.001031777,0.001639666,0.0008801822,0.004761913],"category_scores_gemma":[0.001595943,0.0006956059,0.0004125231,0.0009988886,0.0003331349,0.001027671,0.0012281,0.0006546595,0.002266543],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000516332,"about_ca_system_score_gemma":0.001601323,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001941442,"about_ca_topic_score_gemma":0.00121501,"domain_scores_codex":[0.9985024,0.0001399941,0.00009472743,0.0002913478,0.0008932097,0.0000783684],"domain_scores_gemma":[0.9989405,0.0001382907,0.00006947751,0.0001617542,0.0006308802,0.00005914407],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004006276,0.0001499532,0.008238325,0.0004929049,0.00007765721,0.0007596699,0.0007319684,0.01835395,0.3969029,0.01020825,0.01164658,0.5520372],"study_design_scores_gemma":[0.0002261167,0.0006478014,0.01733663,0.0001563046,0.0001543022,0.002876835,0.0002714412,0.5677608,0.3161208,0.00431196,0.08975894,0.0003780164],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01094415,0.00007690536,0.9815769,0.0001538563,0.00007670261,0.0003993979,0.0002510455,0.004233267,0.00228784],"genre_scores_gemma":[0.1691694,0.0001899742,0.8236433,0.0003169418,0.00008776117,0.001226658,0.0007754934,0.0002365174,0.004354001],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004761913,"threshold_uncertainty_score":0.01593018,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06901258724677838,"score_gpt":0.264659524917653,"score_spread":0.1956469376708747,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}