{"id":"W1966621293","doi":"10.1121/1.1928807","title":"Empirical modeling of human face kinematics during speech using motion clustering","year":2005,"lang":"en","type":"article","venue":"The Journal of the Acoustical Society of America","topic":"Face recognition and analysis","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"National Institute on Deafness and Other Communication Disorders","keywords":"Kinematics; Cluster analysis; Motion (physics); Computer science; Set (abstract data type); Face (sociological concept); Motion capture; Displacement (psychology); Primary (astronomy); Artificial intelligence; Speech recognition; Physics; Psychology; Astrophysics; Linguistics","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":[0.0005557881,0.0001028261,0.0003095844,0.00003390365,0.0001981523,0.00002488847,0.0007858786,0.0000468726,0.00001424146],"category_scores_gemma":[0.00008442386,0.0000596101,0.0004418301,0.0003901649,0.0001683901,0.0001899362,0.0003082975,0.0003020579,0.000001219992],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007279902,"about_ca_system_score_gemma":0.00003500589,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001740539,"about_ca_topic_score_gemma":4.060822e-7,"domain_scores_codex":[0.9984809,0.0001266477,0.000599233,0.00008214016,0.0005358563,0.0001752325],"domain_scores_gemma":[0.9987526,0.0001269872,0.0005645244,0.0002795261,0.0002144097,0.00006195559],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000005669653,0.0000801065,0.00003487271,0.0000437774,0.00008074953,2.807963e-7,0.001176761,0.9219165,0.07167519,0.000001371616,0.00009072623,0.004893932],"study_design_scores_gemma":[0.0001867049,0.00003847151,0.00007202224,0.0001098604,0.0001369972,0.00005206737,0.0009728848,0.9923289,0.005807099,0.000225858,0.000003976036,0.00006518458],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2493023,0.00004094945,0.7484837,0.002071118,0.00003479716,0.00003556487,7.363973e-7,0.000006834244,0.0000239856],"genre_scores_gemma":[0.7545848,0.00005507947,0.2450354,0.0002260508,0.00007891509,8.87491e-8,5.971522e-8,0.000005405056,0.00001408803],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5052826,"threshold_uncertainty_score":0.2430829,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04398977220723408,"score_gpt":0.3095872484431266,"score_spread":0.2655974762358925,"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."}}