{"id":"W2756945571","doi":"10.1109/tits.2019.2904735","title":"Semi-Automated Generation of Road Transition Lines Using Mobile Laser Scanning Data","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Transportation Systems","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":44,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University; University of Waterloo","funders":"National Natural Science Foundation of China","keywords":"Point cloud; Intersection (aeronautics); Thresholding; Computer science; Computer vision; Artificial intelligence; Line (geometry); Laser scanning; Road surface; Overlay; Delaunay triangulation; Voxel; Point (geometry); Algorithm; Laser; Mathematics; Geography; Geometry; Engineering; Optics; Image (mathematics); Physics","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":[0.0004106422,0.0008830892,0.0004931377,0.002446236,0.0003798096,0.0007729111,0.0009919991,0.000519709,0.001918432],"category_scores_gemma":[0.001237688,0.0004656452,0.0007882291,0.001322949,0.0002531511,0.0008181074,0.0007688781,0.0006615404,0.001778481],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002743812,"about_ca_system_score_gemma":0.0007849509,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002987715,"about_ca_topic_score_gemma":0.005591354,"domain_scores_codex":[0.9994779,0.00006632373,0.00002516105,0.0001547791,0.0002099625,0.00006592236],"domain_scores_gemma":[0.9989911,0.0002561339,0.0001452409,0.0002182993,0.0003501827,0.00003892618],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000289787,0.0001714268,0.01144658,0.0003965549,0.0001089047,0.0005010773,0.0005641119,0.09740111,0.1164401,0.002055642,0.006418704,0.764206],"study_design_scores_gemma":[0.00003733343,0.0001403423,0.008324879,0.00004162439,0.0000403802,0.0003742953,0.0003436211,0.906048,0.07482515,0.002174535,0.007579684,0.00007017063],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.08006616,0.0001387506,0.9039034,0.00006472781,0.00003688304,0.0001967282,0.001186101,0.01301909,0.001388022],"genre_scores_gemma":[0.3759282,0.0001340873,0.6188194,0.00003582121,0.00001952806,0.0002091154,0.00330683,0.000593847,0.000953217],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002987715,"threshold_uncertainty_score":0.006417811,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04900244759130435,"score_gpt":0.2889893759739592,"score_spread":0.2399869283826549,"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."}}