{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003623809,0.0001283433,0.0001344915,0.00007917891,0.0001163649,0.0001810063,0.0009786101,0.0001272146,0.0006860897],"category_scores_gemma":[0.00008641229,0.0001118851,0.00007056491,0.0004332468,0.00008308711,0.0004361555,0.0001309146,0.0003690221,0.000434659],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001989249,"about_ca_system_score_gemma":0.00008295134,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002793687,"about_ca_topic_score_gemma":0.000001728368,"domain_scores_codex":[0.9987975,0.00002538559,0.0002103535,0.0003668203,0.0003380543,0.0002619314],"domain_scores_gemma":[0.9989369,0.00005282875,0.00008268085,0.0006708994,0.0001388778,0.0001178302],"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":[0.00001124586,0.00007123296,0.0001163841,0.00000879603,0.000004763707,0.000008338531,0.0001272372,3.588501e-8,0.751958,0.02783558,0.004104543,0.2157538],"study_design_scores_gemma":[0.000258984,0.0001229022,0.004487745,0.00001183912,0.000006194125,0.00003428411,0.000005724777,0.04663606,0.8436517,0.02368713,0.08077671,0.0003207723],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08790627,0.0000248108,0.7409279,0.005456713,0.001348594,0.0004490851,0.000007423278,0.001501721,0.1623774],"genre_scores_gemma":[0.8604088,0.00001510556,0.1365758,0.0004334879,0.0001822397,0.000008389276,0.000002815876,0.00001183001,0.002361493],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7725025,"threshold_uncertainty_score":0.7512202,"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."}}