{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004811823,0.0002996378,0.0002969195,0.0002899678,0.0002407489,0.0005482814,0.0003176593,0.0004065651,0.002306253],"category_scores_gemma":[0.002154134,0.000230854,0.0002944177,0.0001767168,0.0002465979,0.0004506316,0.000362722,0.0003740542,0.0006291533],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004949841,"about_ca_system_score_gemma":0.0002694465,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00369289,"about_ca_topic_score_gemma":0.00242525,"domain_scores_codex":[0.9997416,0.0001053641,0.00001175924,0.00005092125,0.00006927662,0.00002106858],"domain_scores_gemma":[0.9996036,0.0002204819,0.00003243554,0.00003321968,0.00009264254,0.00001780887],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0005985767,0.0001886926,0.004317834,0.0001480484,0.0001083132,0.000136723,0.000327446,0.304029,0.04023027,0.00562642,0.0037167,0.640572],"study_design_scores_gemma":[0.000006374945,0.000037846,0.002124568,0.000006633219,0.000009017092,0.00002653465,0.00002372703,0.991843,0.003404335,0.001984803,0.0005229596,0.00001011264],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03485287,0.0003185994,0.962115,0.000148047,0.00004375341,0.00005673014,0.00006504267,0.001074791,0.001325205],"genre_scores_gemma":[0.8170376,0.0002360155,0.1798559,0.00006339387,0.00004184783,0.0001057392,0.0001726086,0.00005582567,0.002431054],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00369289,"threshold_uncertainty_score":0.007715166,"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."}}