{"id":"W2404156759","doi":"","title":"Automatic Sharp Feature Extraction from Point Clouds with Optimal Neighborhood Size","year":2013,"lang":"en","type":"article","venue":"Machine Vision and Applications","topic":"3D Shape Modeling and Analysis","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université Laval","funders":"","keywords":"Point cloud; Centroid; Robustness (evolution); Histogram; Artificial intelligence; Algorithm; Computer science; Mathematics; Pattern recognition (psychology); Point (geometry); Normal; Projection (relational algebra); Computer vision; Image (mathematics); Surface (topology); 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":[0.00003601244,0.000138898,0.0001404713,0.00004285405,0.0001205265,0.00010283,0.000077002,0.00006036731,0.0005985657],"category_scores_gemma":[0.000004915328,0.0001019214,0.00003821759,0.00017677,0.00001598448,0.0001233716,0.00001788475,0.0001647162,0.0001247414],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001513452,"about_ca_system_score_gemma":0.00000495312,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001097425,"about_ca_topic_score_gemma":0.00001223423,"domain_scores_codex":[0.9994487,0.000009164874,0.000131798,0.0001889438,0.0001027043,0.000118647],"domain_scores_gemma":[0.9995578,0.00006243779,0.00002843415,0.0002270732,0.00002979377,0.00009449007],"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":[0.00001109829,0.0002947681,0.002030941,0.0001134208,0.000305324,0.000002679362,0.0004919213,0.1278662,0.02845005,0.0009850169,0.01183436,0.8276142],"study_design_scores_gemma":[0.0002092171,0.00001897847,0.0062797,0.00002042594,0.00004936077,0.000004116809,0.00004487388,0.990576,0.0001648764,0.0005199814,0.001969474,0.0001430529],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3736422,0.0008443972,0.618894,0.002678265,0.00003204229,0.0004517317,0.00006216038,0.0007845144,0.002610711],"genre_scores_gemma":[0.9892463,0.00006669831,0.01003676,0.0001063213,0.00008034825,0.0001592833,0.00007743646,0.00002447776,0.0002023774],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8627097,"threshold_uncertainty_score":0.6553875,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003290565317453907,"score_gpt":0.2197410333532123,"score_spread":0.2164504680357584,"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."}}