{"id":"W4387421333","doi":"10.1145/3594739.3605106","title":"11th International Workshop on Human Activity Sensing Corpus and Applications (HASCA)","year":2023,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Activity recognition; Session (web analytics); Computer science; Context (archaeology); Wearable computer; Scale (ratio); Human–computer interaction; Ubiquitous computing; Data science; Artificial intelligence; World Wide Web; Embedded system","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":[0.01246433,0.002286997,0.002920863,0.005128782,0.001840348,0.006636544,0.00420886,0.003135624,0.04670122],"category_scores_gemma":[0.02120984,0.001094608,0.002412685,0.003680262,0.001342542,0.007219012,0.007888853,0.004398352,0.03654929],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001896819,"about_ca_system_score_gemma":0.005805543,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01597324,"about_ca_topic_score_gemma":0.02485646,"domain_scores_codex":[0.9935201,0.002449843,0.0006322684,0.001278682,0.001593212,0.0005259134],"domain_scores_gemma":[0.9871697,0.003250069,0.0002025392,0.003047373,0.00465628,0.001674023],"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":[0.0003549492,0.0002429982,0.0009861636,0.0005721496,0.00009319804,0.0001508849,0.0003396805,0.001188268,0.004208962,0.005871843,0.7005367,0.2854541],"study_design_scores_gemma":[0.00005771247,0.0001566218,0.004063602,0.0004914298,0.00005699872,0.0003531648,0.0005155939,0.01260761,0.004328697,0.01275176,0.9645364,0.00008044719],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.01728013,0.05214229,0.5739062,0.035003,0.07044918,0.003367783,0.1095832,0.05761136,0.08065685],"genre_scores_gemma":[0.05447417,0.01772206,0.3845313,0.007945077,0.00747856,0.005388391,0.3281794,0.007623421,0.1866576],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.04670122,"threshold_uncertainty_score":0.1562312,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07986625048859589,"score_gpt":0.3343439606091181,"score_spread":0.2544777101205222,"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."}}