{"id":"W2091777098","doi":"10.1145/2185520.2185538","title":"Eyecatch","year":2012,"lang":"en","type":"article","venue":"ACM Transactions on Graphics","topic":"Human Motion and Animation","field":"Engineering","cited_by":64,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Artificial intelligence; Movement (music); Human–computer interaction; Computer vision","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003807917,0.0007946676,0.0004418685,0.0007144143,0.0006028234,0.001132328,0.001779414,0.001338277,0.02375773],"category_scores_gemma":[0.001223863,0.0004118716,0.0009659615,0.000316503,0.000831264,0.001508815,0.002525552,0.001423158,0.004419622],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000585076,"about_ca_system_score_gemma":0.0006874462,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004079949,"about_ca_topic_score_gemma":0.008444413,"domain_scores_codex":[0.9997658,0.000039243,0.000009845561,0.00007247132,0.00008306509,0.00002952879],"domain_scores_gemma":[0.9997364,0.00005942501,0.0000160058,0.00009808194,0.000045901,0.00004421427],"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":[0.0005595359,0.0002024986,0.001970229,0.0004757881,0.0001739606,0.0008021948,0.0007194621,0.1385494,0.05121302,0.3155594,0.07868021,0.4110943],"study_design_scores_gemma":[0.0001049151,0.0001649696,0.0007586433,0.0001096544,0.0000547681,0.0006303413,0.00007156431,0.6527761,0.01328017,0.08607361,0.2458951,0.00008015923],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005873485,0.0004119757,0.959541,0.0003413241,0.0002436969,0.00015967,0.0007228234,0.008768304,0.02393773],"genre_scores_gemma":[0.2637905,0.0008317562,0.6740938,0.0006673133,0.0001245273,0.0006673945,0.002456006,0.002910165,0.05445853],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02375773,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02404173322913903,"score_gpt":0.239612832632958,"score_spread":0.2155710994038189,"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."}}