{"id":"W2170372557","doi":"10.1109/icme.2009.5202807","title":"Toward natural and efficient human computer interaction","year":2009,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Toronto Metropolitan University","funders":"","keywords":"Computer science; Human–computer interaction; Emotion recognition; Face detection; Facial expression; Focus (optics); Face (sociological concept); Natural (archaeology); Facial recognition system; Artificial intelligence; Emotion detection; Sketch recognition; Pattern recognition (psychology); Gesture recognition","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":[0.00004530339,0.00005415825,0.00005132702,0.00004978017,0.00006878422,0.0001198634,0.0001123115,0.00002058206,0.0000173591],"category_scores_gemma":[0.000001703775,0.0000408658,0.00001892593,0.00006182082,0.000007605189,0.0002100029,0.00005394329,0.00006771419,0.00004276371],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008727829,"about_ca_system_score_gemma":0.000002686864,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006786975,"about_ca_topic_score_gemma":7.292226e-7,"domain_scores_codex":[0.9995672,0.00001420032,0.00007742116,0.0001641208,0.00008560904,0.00009150603],"domain_scores_gemma":[0.9998066,0.00001213491,0.0000205171,0.00009909945,0.00002507662,0.00003659252],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001009192,0.0001808782,0.00006976257,0.00001007189,0.000007955661,0.00001715165,0.001828648,0.0003294199,0.03467078,0.029974,0.01658398,0.9163173],"study_design_scores_gemma":[0.0007683845,0.0003793397,0.02235517,0.00008090962,0.000004376976,0.00009154355,0.00009904794,0.9354941,0.03188229,0.004294921,0.004183639,0.0003662818],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5968522,0.00003300329,0.3959781,0.002293073,0.0005249886,0.00007709912,1.398872e-7,0.0001994671,0.004041885],"genre_scores_gemma":[0.982401,0.000001849019,0.01620886,0.001198715,0.00004776572,8.075463e-7,0.000001704723,0.000001033165,0.0001383044],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9351647,"threshold_uncertainty_score":0.1666459,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01981860532337929,"score_gpt":0.2697615556744895,"score_spread":0.2499429503511102,"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."}}