{"id":"W2131970932","doi":"","title":"SHREC 15 Track Non rigid 3D Shape Retrieval","year":2010,"lang":"en","type":"article","venue":"NPARC","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":76,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Polygon mesh; Computer science; Benchmark (surveying); Object (grammar); Matching (statistics); Track (disk drive); Computer vision; CONTEST; Artificial intelligence; Information retrieval; Mathematics; Computer graphics (images); Geology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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":[0.007974445,0.003854143,0.004213601,0.003750137,0.001711049,0.002716144,0.006078145,0.004074262,0.01368641],"category_scores_gemma":[0.007962861,0.0008366478,0.002693393,0.002720885,0.001190537,0.002991752,0.002468718,0.002361662,0.01227444],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002243605,"about_ca_system_score_gemma":0.001895098,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02106469,"about_ca_topic_score_gemma":0.02809396,"domain_scores_codex":[0.9943158,0.0007406386,0.0002545179,0.0009907169,0.003158098,0.0005403536],"domain_scores_gemma":[0.9920083,0.001077685,0.0001936498,0.002430207,0.003442548,0.0008475087],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001900145,0.001967364,0.001862038,0.001273695,0.0007079829,0.0004242939,0.0001243299,0.01784446,0.04360978,0.002940861,0.523835,0.4035101],"study_design_scores_gemma":[0.001998155,0.008431173,0.01832631,0.0001635526,0.0004393079,0.002590703,0.0005973801,0.4580262,0.2024184,0.005844237,0.3008447,0.0003198344],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"other","genre_scores_codex":[0.3204269,0.01554972,0.305761,0.003593317,0.009995879,0.009820719,0.09804937,0.1472851,0.08951806],"genre_scores_gemma":[0.2044006,0.002121815,0.3170404,0.0009778736,0.0007107387,0.001316372,0.3663205,0.006017867,0.1010938],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.02106469,"threshold_uncertainty_score":0.04578561,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01369679505286773,"score_gpt":0.2545307801711373,"score_spread":0.2408339851182696,"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."}}