{"id":"W4391800849","doi":"10.1145/3613904.3642101","title":"EmoWear: Exploring Emotional Teasers for Voice Message Interaction on Smartwatches","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Emotion and Mood Recognition","field":"Psychology","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"Huawei Technologies (Canada); The Scarborough Hospital; University of Toronto; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; University of Waterloo","keywords":"Mirroring; Computer science; Animation; Human–computer interaction; Tone (literature); Smartwatch; Multimedia; Speech recognition; Psychology; Communication; Wearable computer; Computer graphics (images)","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":[0.000507094,0.0005380869,0.0002642628,0.0002343744,0.0002199125,0.0008761695,0.0004551775,0.0005012894,0.004874462],"category_scores_gemma":[0.001711552,0.0001663113,0.0003585424,0.0001101823,0.0002969888,0.0009233345,0.0008157118,0.0003371067,0.0009319764],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001093938,"about_ca_system_score_gemma":0.0001002398,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001178824,"about_ca_topic_score_gemma":0.0002642721,"domain_scores_codex":[0.9997435,0.0001096229,0.00001106354,0.00005562626,0.00004676171,0.00003345994],"domain_scores_gemma":[0.9994165,0.0004245071,0.00003987736,0.00004004115,0.00004370667,0.00003537217],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00135975,0.0006915954,0.003814891,0.001362164,0.00008062572,0.0006399183,0.006044673,0.001702591,0.7401993,0.003080627,0.003042573,0.2379812],"study_design_scores_gemma":[0.0008340496,0.01280939,0.1206949,0.00076114,0.000711768,0.003884278,0.009668775,0.1000919,0.6301122,0.01442493,0.1055952,0.0004113666],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7715467,0.001000877,0.2106569,0.0002947402,0.000149432,0.0005566038,0.000269064,0.00249249,0.01303306],"genre_scores_gemma":[0.8823776,0.0006005851,0.1064229,0.0003079506,0.00007407284,0.0007184451,0.0002942198,0.0003231348,0.008881191],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004874462,"threshold_uncertainty_score":0.0163067,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1724574834261333,"score_gpt":0.3786686334431542,"score_spread":0.2062111500170208,"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."}}