{"id":"W2000982104","doi":"10.1109/tro.2007.904899","title":"Affective State Estimation for Human–Robot Interaction","year":2007,"lang":"en","type":"article","venue":"IEEE Transactions on Robotics","topic":"Emotion and Mood Recognition","field":"Psychology","cited_by":224,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Robot; Hidden Markov model; Human–robot interaction; Artificial intelligence; Computer science; Computer vision; Simulation","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":[0.0003158634,0.0001662654,0.0001493344,0.0002965805,0.0002800789,0.00002929959,0.00006470043,0.0001386887,0.0003059021],"category_scores_gemma":[0.000006895323,0.0001781312,0.0001442672,0.0001846054,0.00004490403,0.0001487825,3.976486e-7,0.0002696399,0.0003316941],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001382529,"about_ca_system_score_gemma":0.00001296013,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004204936,"about_ca_topic_score_gemma":0.0002937719,"domain_scores_codex":[0.9989242,0.00006037955,0.000310034,0.0002837762,0.000130748,0.0002908419],"domain_scores_gemma":[0.9991795,0.0002801149,0.000119452,0.0001938197,0.0001352552,0.00009181138],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0005186676,0.001298475,0.00001186124,0.00004336067,0.0002095441,0.000007348576,0.002078544,0.6552818,0.004764992,0.002118007,0.0007253923,0.332942],"study_design_scores_gemma":[0.03477739,0.01537101,0.02646199,0.001059393,0.002549863,0.000629524,0.01234433,0.1658824,0.6803561,0.04988457,0.005027969,0.005655428],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0148671,0.000005317801,0.9768312,0.0001710512,0.003575754,0.0006416392,0.00003094801,0.000204163,0.003672847],"genre_scores_gemma":[0.9834904,0.000004189941,0.01256531,0.0002315084,0.00008202642,0.00007463623,0.0000346023,0.00004019158,0.003477116],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9686233,"threshold_uncertainty_score":0.7263981,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05072737428589986,"score_gpt":0.3726119016628146,"score_spread":0.3218845273769148,"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."}}