{"id":"W4246299849","doi":"10.5194/isprsarchives-xli-b1-545-2016","title":"A FEASIBILITY STUDY ON USE OF GENERIC MOBILE LASER SCANNING SYSTEM FOR DETECTING ASPHALT PAVEMENT CRACKS","year":2016,"lang":"en","type":"article","venue":"The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Point cloud; Noise (video); Digital elevation model; Filter (signal processing); Laser scanning; Computer science; Elevation (ballistics); Geology; Remote sensing; Lidar; Computer vision; Laser; Structural engineering; Engineering; Image (mathematics); Optics; Physics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0007346621,0.0003611116,0.0002240296,0.0006295894,0.0003683629,0.0004427926,0.0005786455,0.0009098441,0.001360955],"category_scores_gemma":[0.001280891,0.0001626826,0.0002667173,0.0002811521,0.0003244953,0.001070614,0.0003614046,0.0001869531,0.0003271234],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003715868,"about_ca_system_score_gemma":0.0005703921,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00314543,"about_ca_topic_score_gemma":0.004264454,"domain_scores_codex":[0.9991457,0.0002012604,0.0000271414,0.0002257533,0.000300995,0.0000990613],"domain_scores_gemma":[0.9990153,0.0002301771,0.00008962513,0.0001198751,0.0004666676,0.00007815096],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001087016,0.0006173159,0.1692672,0.0006802146,0.00006624775,0.002356858,0.001271777,0.01155131,0.564912,0.001529079,0.001386687,0.2452744],"study_design_scores_gemma":[0.0002438853,0.01019648,0.3059023,0.0001560693,0.0003598383,0.004501266,0.004776169,0.3176059,0.3388482,0.001021358,0.01623237,0.0001561559],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9375847,0.0002468986,0.05878351,0.0001964917,0.00002570918,0.0002406751,0.0001416938,0.0004543062,0.002325973],"genre_scores_gemma":[0.9714357,0.0001092453,0.02770497,0.00003133947,0.000009803809,0.0000520623,0.00009406411,0.000009894911,0.0005528539],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00314543,"threshold_uncertainty_score":0.006254256,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0254786131942329,"score_gpt":0.2616848501877574,"score_spread":0.2362062369935246,"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."}}