{"id":"W2954022589","doi":"10.5194/ica-adv-1-12-2019","title":"Automated Extraction of Driving Lines from Mobile Laser Scanning Point Clouds","year":2019,"lang":"en","type":"article","venue":"Advances in Cartography and GIScience of the ICA","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University; University of Waterloo","funders":"","keywords":"Point cloud; Laser scanning; Extraction (chemistry); Computer science; Laser; Artificial intelligence; Optics; Physics; Chromatography; Chemistry","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001676654,0.00006151233,0.000101868,0.00003659122,0.00007177366,0.000008358943,0.0001790862,0.00002648802,0.00002950765],"category_scores_gemma":[0.00001728924,0.00004349772,0.00004770285,0.0005648157,0.0004359673,0.0002695053,0.00008004912,0.00006591804,0.000002921611],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009308306,"about_ca_system_score_gemma":0.000005422065,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000405825,"about_ca_topic_score_gemma":0.0001727203,"domain_scores_codex":[0.9993249,0.00002862175,0.0001662206,0.0001938043,0.0001770802,0.000109341],"domain_scores_gemma":[0.9995189,0.00008435198,0.0001289611,0.0002356956,0.000007948091,0.00002413773],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000007294297,0.0000527699,0.649574,0.000009400775,0.00000304827,1.643934e-7,0.0008068244,0.03649433,0.2885957,0.00005268943,0.00004181176,0.02436196],"study_design_scores_gemma":[0.000237761,0.00007619098,0.8593495,0.0001806507,0.00001274364,0.000004059681,0.001003296,0.02908487,0.1015365,0.003262745,0.005085745,0.0001659656],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.996163,0.0002515013,0.0001276905,0.00006809898,0.0001376328,0.0001290222,0.000003592238,0.00001949428,0.003099962],"genre_scores_gemma":[0.9971761,0.0001381536,0.00262916,0.00001722147,0.000007345488,0.000002120836,6.112577e-7,0.00000290156,0.00002636822],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2097755,"threshold_uncertainty_score":0.1773786,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003621875473499219,"score_gpt":0.2474946810574831,"score_spread":0.2438728055839839,"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."}}