{"id":"W2724314443","doi":"10.1145/3072959.3092817","title":"Co-Locating Style-Defining Elements on 3D Shapes","year":2017,"lang":"en","type":"article","venue":"ACM Transactions on Graphics","topic":"Color perception and design","field":"Psychology","cited_by":59,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University; Carleton University","funders":"Science and Technology Planning Project of Guangdong Province; National Key Research and Development Program of China; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Style (visual arts); Set (abstract data type); Discriminative model; Computer science; Variety (cybernetics); Selection (genetic algorithm); Artificial intelligence; Style analysis; Pattern recognition (psychology); Geography","routes":{"ca_aff":true,"ca_fund":true,"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":["sts","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0003327629,0.0001936239,0.0001741157,0.000269414,0.001380467,0.0001289257,0.0005487206,0.0001709034,0.004416907],"category_scores_gemma":[0.00006807377,0.0001984124,0.0001437312,0.0001253172,0.0001382717,0.0001190279,0.000003997797,0.0004858577,0.001674652],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003048141,"about_ca_system_score_gemma":0.00002188875,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001057778,"about_ca_topic_score_gemma":0.0001706169,"domain_scores_codex":[0.9986345,0.00009972929,0.0002867612,0.0003982934,0.0002586462,0.0003220245],"domain_scores_gemma":[0.9983124,0.0002445559,0.0001597442,0.001124296,0.00005242577,0.0001065889],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0009995929,0.002485486,0.01463446,0.00004549299,0.0006838122,0.00006492971,0.0084431,0.0002794434,0.002039397,0.03276097,0.008793334,0.92877],"study_design_scores_gemma":[0.01200391,0.00487172,0.8001812,0.0004746166,0.0005224125,0.00008586035,0.007442464,0.003051724,0.001419661,0.008924278,0.1582453,0.002776857],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6219927,0.0001009458,0.1322075,0.006360334,0.003954811,0.001242457,0.0002923181,0.001043401,0.2328055],"genre_scores_gemma":[0.9950082,0.00005138301,0.001444175,0.001700385,0.00005731852,0.00009308651,0.000009627684,0.00003500433,0.001600858],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9259931,"threshold_uncertainty_score":0.9999196,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07813963579146364,"score_gpt":0.3790163807634357,"score_spread":0.300876744971972,"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."}}