{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0003225196,0.00031529,0.0002600879,0.0003550834,0.00008760678,0.0001143618,0.0001480083,0.0003599608,0.003663437],"category_scores_gemma":[0.00006153164,0.0002954328,0.0003693039,0.00008027443,0.0000301662,0.00007779543,0.0001908871,0.0009174029,0.003958721],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001536695,"about_ca_system_score_gemma":0.00005174024,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009905959,"about_ca_topic_score_gemma":0.00006198783,"domain_scores_codex":[0.9982543,0.0001063732,0.0003770535,0.0007786035,0.0002056561,0.0002780319],"domain_scores_gemma":[0.9990682,0.000236992,0.0001291614,0.0003507883,0.0001116089,0.0001032393],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001252571,0.001664402,0.0002162211,0.002267213,0.002185782,0.00006467682,0.01000916,0.0006833608,0.0005092911,0.07208665,0.5328315,0.3762291],"study_design_scores_gemma":[0.006093502,0.001798565,0.01168049,0.008319171,0.001540312,0.0001818706,0.03422931,0.00431997,0.01030281,0.1879418,0.729214,0.00437818],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.4211324,0.0002057523,0.01108949,0.007707087,0.05241172,0.002083286,0.0004301996,0.001363991,0.503576],"genre_scores_gemma":[0.9350622,0.00005831301,0.001174121,0.0008989057,0.002082345,0.0009898946,0.0009366227,0.0001102963,0.05868732],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5139297,"threshold_uncertainty_score":0.9999498,"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."}}