{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006399894,0.00007076134,0.00005009562,0.0001127959,0.00008068408,0.00001240315,0.00007717249,0.00005381395,0.0003278402],"category_scores_gemma":[0.000003415648,0.00007366914,0.00005283057,0.0001874188,0.00001523869,0.0001608126,4.872498e-7,0.0001517496,0.0002765488],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001535317,"about_ca_system_score_gemma":0.000002019308,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001344791,"about_ca_topic_score_gemma":0.00000614838,"domain_scores_codex":[0.9996237,0.000008985762,0.00008578358,0.00004965027,0.0000852795,0.0001465688],"domain_scores_gemma":[0.9996897,0.00002298423,0.000006133198,0.0002000625,0.00001229693,0.00006879531],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00006671903,0.00285336,0.01078757,0.0007414531,0.0009011403,0.00000642187,0.01523143,0.07202289,0.0315464,0.113729,0.02652018,0.7255934],"study_design_scores_gemma":[0.003324513,0.0003860888,0.2302692,0.0002216968,0.0003724002,0.00007282867,0.001249334,0.04689237,0.07636868,0.02304561,0.6146927,0.003104612],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2142188,0.000170623,0.7753979,0.0004146637,0.001148637,0.0001382013,0.00001584174,0.00109762,0.007397705],"genre_scores_gemma":[0.9986196,0.0001197776,0.0009205755,0.0001426026,0.00005198639,0.00001180459,0.000004060777,0.00001695192,0.0001125969],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7844008,"threshold_uncertainty_score":0.358962,"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."}}