{"id":"W1940445245","doi":"10.5220/0004674801120119","title":"Automatic Method for Sharp Feature Extraction from 3D Data of Man-made Objects","year":2014,"lang":"en","type":"article","venue":"","topic":"3D Shape Modeling and Analysis","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"","keywords":"Point cloud; Histogram; Computer science; Robustness (evolution); Centroid; Polygon mesh; Artificial intelligence; Pattern recognition (psychology); Feature extraction; Computer vision; Noise (video); Segmentation; Normal; Point (geometry); Algorithm; Mathematics; Surface (topology); Image (mathematics)","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":[0.0002221223,0.00009023314,0.0001904499,0.00005646153,0.00002325986,0.00001896273,0.0001948221,0.00007771157,0.0001446201],"category_scores_gemma":[0.00008315902,0.00007784561,0.00005250409,0.00007718635,0.000003589827,0.0001177709,0.00002560267,0.00007741004,0.0000140792],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001097111,"about_ca_system_score_gemma":0.000004619621,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001145272,"about_ca_topic_score_gemma":0.00007963477,"domain_scores_codex":[0.9994687,0.00001891552,0.0001443832,0.0001675162,0.0000914866,0.0001089288],"domain_scores_gemma":[0.9992311,0.0002161269,0.00003017056,0.0004684081,0.00002213272,0.00003206353],"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":[0.000009769877,0.00006202077,0.00009113501,0.0005594814,0.0006534953,9.013662e-7,0.0005219519,0.3728803,0.09272163,0.0001455618,0.03494842,0.4974053],"study_design_scores_gemma":[0.000134341,0.000007306899,0.0001086867,0.00002932111,0.000130762,4.894342e-7,0.00004729169,0.9923656,0.005690721,0.0003163001,0.001077767,0.00009143607],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01016077,0.000133861,0.9876512,0.00008620265,0.0001020238,0.00006071367,0.00006998677,0.000235011,0.001500209],"genre_scores_gemma":[0.551072,0.000008807019,0.4481464,0.00003345586,0.00009504049,0.000005181636,0.00022328,0.0000175654,0.0003982761],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6194852,"threshold_uncertainty_score":0.3174452,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03042733733828245,"score_gpt":0.3054026608693877,"score_spread":0.2749753235311052,"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."}}