{"id":"W4246934379","doi":"10.1002/cav.240","title":"Multi‐resolution parametric synthesis of manipulative dynamic textures","year":2008,"lang":"en","type":"article","venue":"Computer Animation and Virtual Worlds","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"University of British Columbia; Chinese University of Hong Kong; University of Hong Kong; University of Alberta","keywords":"Computer science; Feature (linguistics); Parametric statistics; Scale (ratio); Artificial intelligence; Computer vision; Motion (physics); Pattern recognition (psychology); Mathematics","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":[0.0001972136,0.0003628392,0.0002973342,0.0003677201,0.0001058863,0.0003459418,0.0002730419,0.0002294443,0.001620129],"category_scores_gemma":[0.0006749951,0.0001949452,0.0003712824,0.0002947806,0.0002476689,0.0003798916,0.0004403061,0.0003025984,0.0002403592],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001639947,"about_ca_system_score_gemma":0.0001117476,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002655868,"about_ca_topic_score_gemma":0.0002676114,"domain_scores_codex":[0.9998643,0.00002215119,0.000006702468,0.00002941381,0.00006268953,0.00001471596],"domain_scores_gemma":[0.9997563,0.0000951832,0.0000335248,0.00005882018,0.00003804756,0.00001823555],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002497458,0.00004408633,0.0003459645,0.0001586827,0.0000418801,0.0002188097,0.0001690632,0.1194958,0.678605,0.008720412,0.0007668959,0.1911836],"study_design_scores_gemma":[0.00003325116,0.0001593502,0.0009283046,0.00001644058,0.00002330335,0.0002855337,0.00004515128,0.7773638,0.2096605,0.002906209,0.008542862,0.00003525465],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1183446,0.0002119951,0.8772416,0.00008315755,0.00006286388,0.00004230451,0.00008970106,0.0005857534,0.003338074],"genre_scores_gemma":[0.7188709,0.000189782,0.2778448,0.00003193123,0.00003236042,0.00006633256,0.0001219465,0.00009969631,0.002742182],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001620129,"threshold_uncertainty_score":0.00541991,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0377807458063802,"score_gpt":0.2932110846152996,"score_spread":0.2554303388089194,"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."}}