{"id":"W2620878206","doi":"10.3390/infrastructures2020008","title":"An Enhanced Algorithm for Concurrent Recognition of Rail Tracks and Power Cables from Terrestrial and Airborne LiDAR Point Clouds","year":2017,"lang":"en","type":"article","venue":"Infrastructures","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Point cloud; Lidar; Computer science; Point (geometry); Process (computing); Algorithm; Remote sensing; Identification (biology); Artificial intelligence; Geology; Mathematics; Geometry","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":[0.00007959893,0.0001066531,0.0001446564,0.00001402844,0.0002270579,0.00008463229,0.0001151405,0.00007491557,0.0001139515],"category_scores_gemma":[0.00004742944,0.00009228801,0.00002646749,0.00001513571,0.0003524479,0.0001514998,0.00004539848,0.0000669747,0.000002203626],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001464531,"about_ca_system_score_gemma":0.000008432155,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001153001,"about_ca_topic_score_gemma":0.000152748,"domain_scores_codex":[0.9993433,0.00002203955,0.0001611733,0.0002547564,0.00009873422,0.0001200195],"domain_scores_gemma":[0.9994439,0.00005251852,0.0001572434,0.0002590237,0.000007725122,0.00007951856],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00002993471,0.00001638262,0.0004593067,0.000002047541,0.00001027631,3.234103e-7,0.0005497716,0.000009904473,0.06314065,0.000005118768,0.0002964223,0.9354799],"study_design_scores_gemma":[0.002429659,0.0004567922,0.7884552,0.00004983684,0.00006812995,0.00001036255,0.000339975,0.005413931,0.1707079,0.02822744,0.00347649,0.0003641749],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9861873,0.00004089273,0.01254302,0.00009609276,0.0002103861,0.0002305401,0.0002105373,0.00001533499,0.0004659187],"genre_scores_gemma":[0.9766117,0.00002019682,0.02315379,0.00002996337,0.0001120287,0.00000375651,0.00004941493,0.000009095845,0.00001009366],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9351157,"threshold_uncertainty_score":0.3763396,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01144358538645194,"score_gpt":0.2630702530995203,"score_spread":0.2516266677130683,"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."}}