{"id":"W4404915309","doi":"10.1109/vis55277.2024.00016","title":"Visualizations on Smart Watches while Running: It Actually Helps!","year":2024,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Smartwatch; Computer science; Human–computer interaction; Embedded system; Wearable computer","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":[0.0008734364,0.001028805,0.0005619011,0.0006440167,0.0005654934,0.001580372,0.0006820454,0.000875059,0.01435434],"category_scores_gemma":[0.008413455,0.0003742907,0.0005009054,0.0004522546,0.000353319,0.003114224,0.001361174,0.0007364429,0.002979212],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001454225,"about_ca_system_score_gemma":0.0002213174,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001337963,"about_ca_topic_score_gemma":0.00440702,"domain_scores_codex":[0.9994705,0.0002220994,0.000029895,0.0001287032,0.00008292248,0.00006583567],"domain_scores_gemma":[0.9965398,0.001709008,0.0003007909,0.0004138981,0.0004740032,0.0005625267],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.004712653,0.002212079,0.1003927,0.003912392,0.0006284943,0.001132493,0.01451354,0.002891251,0.06511535,0.002097576,0.09644603,0.7059454],"study_design_scores_gemma":[0.001415667,0.01354601,0.5421858,0.004602005,0.002042029,0.003327807,0.02739559,0.02964133,0.03444604,0.01646113,0.3239142,0.001022513],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9086439,0.004824673,0.04203806,0.004381396,0.001058717,0.0004089778,0.003142842,0.01188202,0.02361941],"genre_scores_gemma":[0.9108396,0.002434611,0.07166304,0.00109583,0.0004782823,0.00030463,0.00245727,0.0008497591,0.009877069],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01435434,"threshold_uncertainty_score":0.04802006,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06152510419174516,"score_gpt":0.3071728208441797,"score_spread":0.2456477166524346,"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."}}