{"id":"W2468212864","doi":"10.1145/2897824.2925984","title":"JALI","year":2016,"lang":"en","type":"article","venue":"ACM Transactions on Graphics","topic":"Face recognition and analysis","field":"Computer Science","cited_by":174,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Foundation for Innovation","keywords":"Computer science; Animation; Computer facial animation; Facial motion capture; Retargeting; Motion capture; Facial muscles; Computer animation; Articulation (sociology); Workflow; Viseme; Speech recognition; Human–computer interaction; Artificial intelligence; Motion (physics); Computer graphics (images); Speech synthesis; Communication; Facial recognition system; Pattern recognition (psychology); Psychology; Face detection","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001010447,0.001049846,0.000633936,0.001464696,0.001104022,0.003380837,0.001878429,0.001376984,0.2429628],"category_scores_gemma":[0.002428539,0.0006440418,0.0007520324,0.001084902,0.0004274083,0.002660562,0.003059032,0.001398357,0.2000176],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000591086,"about_ca_system_score_gemma":0.0008233602,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001203914,"about_ca_topic_score_gemma":0.002239751,"domain_scores_codex":[0.9991762,0.00008563538,0.00006228771,0.0002099928,0.0003738595,0.00009195856],"domain_scores_gemma":[0.9989375,0.0001312966,0.00006118483,0.0003693991,0.0003534842,0.0001470592],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006076162,0.0001340074,0.001259772,0.0006548691,0.00004386195,0.0003297286,0.0004156945,0.001429732,0.03113787,0.03748691,0.4735251,0.4529749],"study_design_scores_gemma":[0.00004145976,0.00009972891,0.001042817,0.00007265466,0.00001762151,0.0005575098,0.0000860662,0.005703108,0.008840171,0.006071729,0.9774145,0.00005264625],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"other","genre_gemma":"methods","genre_scores_codex":[0.0101764,0.002204562,0.272259,0.002037003,0.002611922,0.0006359973,0.02232208,0.1142828,0.5734702],"genre_scores_gemma":[0.07118178,0.002199589,0.2471354,0.002378759,0.0007546966,0.001148793,0.0662591,0.02040746,0.5885344],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.2429628,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02378708272425275,"score_gpt":0.2459245240169211,"score_spread":0.2221374412926683,"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."}}