{"id":"W1986004030","doi":"10.1109/icmew.2012.109","title":"Non-rigid 3D Model Retrieval Using Set of Local Statistical Features","year":2012,"lang":"en","type":"article","venue":"","topic":"3D Shape Modeling and Analysis","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"McGill University","keywords":"Computer science; Set (abstract data type); Polygon (computer graphics); 3d model; Manifold (fluid mechanics); Algorithm; Feature (linguistics); Statistical model; Artificial intelligence; Pattern recognition (psychology)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007585114,0.0009990737,0.001920475,0.004241746,0.0005193719,0.001620212,0.002041839,0.001198262,0.001772581],"category_scores_gemma":[0.00230048,0.0005115494,0.001920229,0.003646829,0.0005811263,0.002922877,0.001652886,0.0007002358,0.001532712],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008140574,"about_ca_system_score_gemma":0.0007544254,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003719644,"about_ca_topic_score_gemma":0.004127497,"domain_scores_codex":[0.9989675,0.00008972739,0.00005877814,0.0002440095,0.0005608861,0.00007903191],"domain_scores_gemma":[0.9991208,0.0002116862,0.0001375232,0.0002915255,0.000200669,0.0000377197],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002062376,0.0001443147,0.001744979,0.0001743572,0.0001659985,0.00021482,0.0001112418,0.04775556,0.06151988,0.005562257,0.004440433,0.87796],"study_design_scores_gemma":[0.00002685641,0.0001470718,0.003439873,0.00001673049,0.00008427015,0.0008235694,0.00009498911,0.9511369,0.03243758,0.007209084,0.004510531,0.00007251893],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02127821,0.0004755674,0.9742869,0.00006026795,0.00003192691,0.00007727393,0.0002231905,0.002654236,0.0009124161],"genre_scores_gemma":[0.314203,0.000752585,0.67931,0.0001252322,0.00007989857,0.0001858279,0.002771885,0.0003805166,0.002191083],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004241746,"threshold_uncertainty_score":0.007396042,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02427755166963968,"score_gpt":0.2691589524591027,"score_spread":0.244881400789463,"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."}}