{"id":"W2253626339","doi":"10.3390/s16020140","title":"Segmentation of Planar Surfaces from Laser Scanning Data Using the Magnitude of Normal Position Vector for Adaptive Neighborhoods","year":2016,"lang":"en","type":"article","venue":"Sensors","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":42,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Myongji University; York University","keywords":"Segmentation; Laser scanning; Computer science; Artificial intelligence; Correctness; Point cloud; Offset (computer science); Laser; Computer vision; Pattern recognition (psychology); Normal; Mathematics; Algorithm; Optics; Surface (topology); Geometry; Physics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001285939,0.00006197808,0.00008006198,0.00001292207,0.00008043462,0.000006245954,0.0001480071,0.00002734739,0.000053478],"category_scores_gemma":[0.00002266229,0.00003884561,0.00002331122,0.00007726202,0.0001263102,0.0001172573,0.00004607234,0.00002394826,0.000007989489],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000370413,"about_ca_system_score_gemma":0.000008358097,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001941458,"about_ca_topic_score_gemma":0.000151936,"domain_scores_codex":[0.9994049,0.0000450523,0.0001481418,0.0001647292,0.0001415186,0.00009565455],"domain_scores_gemma":[0.9993759,0.0001662377,0.0001347816,0.0002861241,0.00001517266,0.00002171897],"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":[0.00008502074,0.00003481572,0.006564992,0.00000420584,0.00003454899,3.086188e-7,0.0007233638,0.007721258,0.9748591,0.00003912604,0.0003189515,0.009614265],"study_design_scores_gemma":[0.0008563719,0.0001405191,0.1915475,0.0001090086,0.0001593838,0.000004261349,0.001783643,0.1188406,0.6853372,0.0005954614,0.0003927478,0.0002332582],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9880939,0.000008827955,0.01067919,0.0002299629,0.00003714618,0.0001919169,0.0004337949,0.000008338717,0.0003169572],"genre_scores_gemma":[0.9730802,0.000003931362,0.02675421,0.0000189429,0.00002929889,6.39279e-7,0.00005372881,0.00000770587,0.00005138708],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2895219,"threshold_uncertainty_score":0.2934917,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03543100320739721,"score_gpt":0.274507318518145,"score_spread":0.2390763153107478,"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."}}