{"id":"W3033223597","doi":"10.3390/robotics9020044","title":"User Affect Elicitation with a Socially Emotional Robot","year":2020,"lang":"en","type":"article","venue":"Robotics","topic":"Emotion and Mood Recognition","field":"Psychology","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"AGE-WELL; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Canadian Institute for Advanced Research","keywords":"Affect (linguistics); Robot; Computer science; Artificial intelligence; Electroencephalography; Human–computer interaction; Valence (chemistry); Affective computing; Human–robot interaction; Arousal; Support vector machine; Machine learning; Psychology; Social psychology; Communication","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":[0.0005790656,0.000389981,0.0002377438,0.0001677197,0.0001416003,0.0003192325,0.0002144873,0.0003220602,0.001287348],"category_scores_gemma":[0.00235824,0.0001206715,0.0002494802,0.00009028418,0.0002757286,0.0002737663,0.0004775749,0.000236243,0.000270418],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001281797,"about_ca_system_score_gemma":0.00006925558,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001999951,"about_ca_topic_score_gemma":0.0003325957,"domain_scores_codex":[0.9995306,0.0002373836,0.0000248596,0.00006872194,0.00009728907,0.00004105407],"domain_scores_gemma":[0.9991709,0.0004613149,0.0001052283,0.00008504626,0.0001227076,0.00005491161],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002092839,0.0006629708,0.02667456,0.0005628108,0.000129819,0.0006486221,0.003444798,0.007756266,0.7629161,0.0006359534,0.00126791,0.1932074],"study_design_scores_gemma":[0.000170427,0.01179602,0.3169462,0.00008010121,0.0003586712,0.002050861,0.002165216,0.2103083,0.4478978,0.001299823,0.006735659,0.0001909284],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9299913,0.00006701209,0.06756147,0.00008123287,0.00003572737,0.0001455463,0.00006351007,0.0004321135,0.001622021],"genre_scores_gemma":[0.9807383,0.00003823157,0.01805028,0.00005250349,0.00001520541,0.00008861261,0.00003835852,0.00001577092,0.0009626949],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001287348,"threshold_uncertainty_score":0.004306614,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04866689032345786,"score_gpt":0.3140236274644404,"score_spread":0.2653567371409825,"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."}}