{"id":"W3134153081","doi":"10.1145/3434074.3447218","title":"Developing a Data-Driven Categorical Taxonomy of Emotional Expressions in Real World Human Robot Interactions","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Emotion and Mood Recognition","field":"Psychology","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Categorical variable; Computer science; Variety (cybernetics); Human–robot interaction; Robot; Annotation; Artificial intelligence; Facial expression; Emotional expression; Set (abstract data type); Expression (computer science); Natural language processing; Human–computer interaction; Machine learning; Cognitive psychology; Psychology","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":[0.001210583,0.001184754,0.0004942961,0.001671577,0.00056808,0.001394119,0.001177914,0.0008579395,0.001457359],"category_scores_gemma":[0.004890773,0.000306534,0.0008828208,0.001032831,0.0004514703,0.001557638,0.001311154,0.001729249,0.001609395],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001040311,"about_ca_system_score_gemma":0.0006736446,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006527025,"about_ca_topic_score_gemma":0.01336174,"domain_scores_codex":[0.9984211,0.0004272531,0.000116473,0.0005903143,0.0002876839,0.0001571321],"domain_scores_gemma":[0.9982493,0.0005265975,0.0001902186,0.0002664631,0.0006114224,0.0001561129],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"qualitative","study_design_scores_codex":[0.001423784,0.00151308,0.06439717,0.001783334,0.0003382515,0.0006301174,0.00284161,0.04289414,0.08599889,0.008337042,0.07871233,0.7111303],"study_design_scores_gemma":[0.00007624762,0.0005394624,0.08892404,0.0002203619,0.00009868266,0.000442682,0.003594828,0.8369789,0.02495499,0.01444085,0.02958112,0.0001477636],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3766844,0.002565065,0.5645843,0.001825639,0.0005808953,0.001521719,0.03184773,0.008555567,0.01183471],"genre_scores_gemma":[0.5774267,0.0005276451,0.3709118,0.0004467532,0.0001451897,0.001455487,0.04479289,0.0002389599,0.004054593],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006527025,"threshold_uncertainty_score":0.01297808,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.325073915229149,"score_gpt":0.4355969182021474,"score_spread":0.1105230029729984,"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."}}