{"id":"W2074582715","doi":"10.1016/j.procs.2011.07.077","title":"An Adaptive Context-Aware and Event-Based Framework Design Model","year":2011,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Event (particle physics); Publication; Context (archaeology); Leverage (statistics); Context model; Context awareness; Adaptation (eye); Ubiquitous computing; Data science; World Wide Web; Human–computer interaction; Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001292875,0.000316652,0.0003159473,0.0003570065,0.000455504,0.0005454079,0.002283219,0.0001174395,0.000007325543],"category_scores_gemma":[0.000077393,0.0003014025,0.00005959782,0.001123166,0.0005303491,0.003422551,0.0004655809,0.0002815518,0.00004499951],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008895158,"about_ca_system_score_gemma":0.0009014186,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003653964,"about_ca_topic_score_gemma":0.00001086229,"domain_scores_codex":[0.996802,0.0001496926,0.0003580875,0.001328176,0.0007292288,0.0006327837],"domain_scores_gemma":[0.9974726,0.000279981,0.0002172392,0.0009126035,0.0006160273,0.0005014815],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001577915,0.0009565968,0.003221687,0.00008633817,0.00004284418,0.00007323669,0.02475671,0.009286654,0.001381522,0.06034308,0.0004370188,0.8992565],"study_design_scores_gemma":[0.0002849029,0.0005063609,0.001095661,0.00009804932,0.000005596791,0.00004099084,0.00005084251,0.9820518,0.004651489,0.0108021,0.00001355645,0.0003986045],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007792454,0.00006420911,0.9902751,0.0001527932,0.000628677,0.0005438063,0.000003327311,0.0004534196,0.00008623143],"genre_scores_gemma":[0.6183885,0.000001452353,0.3806807,0.0008006801,0.00005824639,0.0000540549,3.490558e-7,0.00001062731,0.000005472761],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9727652,"threshold_uncertainty_score":0.9999438,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08759148297205874,"score_gpt":0.2743597219543804,"score_spread":0.1867682389823216,"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."}}