{"id":"W2122069771","doi":"10.1109/icdim.2008.4746831","title":"Mobile Ontology-based Reasoning and Feedback (MORF) Health Monitoring System","year":2008,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lakehead University","funders":"","keywords":"Ontology; Computer science; Focus (optics); Human–computer interaction; Ubiquitous computing; Data science; Software","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":[0.0005555471,0.0004336603,0.0006885704,0.0009640148,0.0005649994,0.0007641995,0.0008474495,0.00077273,0.005380634],"category_scores_gemma":[0.00153373,0.0001499752,0.0004216844,0.0003707066,0.0002281216,0.0008368136,0.0006654771,0.0004306429,0.001550327],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005903972,"about_ca_system_score_gemma":0.000798816,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005645529,"about_ca_topic_score_gemma":0.004497327,"domain_scores_codex":[0.9996301,0.00004086034,0.00003374016,0.0001149628,0.0001353226,0.00004495212],"domain_scores_gemma":[0.9995719,0.0001293198,0.00005266724,0.00005770299,0.0001293307,0.00005915041],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001522646,0.0007715922,0.009345263,0.0005638162,0.0001574269,0.001904902,0.0008998507,0.02208675,0.07570629,0.01753625,0.04948474,0.8200205],"study_design_scores_gemma":[0.0005025972,0.0006052622,0.01060845,0.0001803186,0.0004302385,0.002796141,0.0004146079,0.7548766,0.07280552,0.01921682,0.137309,0.0002544369],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08330506,0.001136171,0.8326704,0.001686133,0.0003387325,0.000678997,0.003296242,0.05183626,0.02505201],"genre_scores_gemma":[0.7774386,0.0004593552,0.2104971,0.000869279,0.0001335511,0.0003308441,0.00155063,0.0002417508,0.008478782],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005645529,"threshold_uncertainty_score":0.01800001,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03798056511767437,"score_gpt":0.2713534096107399,"score_spread":0.2333728444930656,"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."}}