{"id":"W2108732404","doi":"10.1504/ijhtm.2009.023726","title":"Ubiquitous and personalised contextual biomonitoring","year":2009,"lang":"en","type":"article","venue":"International Journal of Healthcare Technology and Management","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Biomonitoring; Context (archaeology); Computer science; Adaptation (eye); Quality (philosophy); Inference; Data science; Geography; Ecology; Artificial intelligence; Psychology","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":[0.0008133972,0.000459312,0.000496154,0.0004430419,0.000401401,0.001292454,0.0009481694,0.00103996,0.002122943],"category_scores_gemma":[0.002504202,0.0002677803,0.0004169005,0.0004483494,0.0004340076,0.001645027,0.001853381,0.0007005659,0.0005802004],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002704165,"about_ca_system_score_gemma":0.000397498,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00204413,"about_ca_topic_score_gemma":0.002711397,"domain_scores_codex":[0.9991634,0.0002550545,0.00005267872,0.0002483747,0.0002065049,0.00007398382],"domain_scores_gemma":[0.9989415,0.0003862609,0.00007848792,0.0003269541,0.0001960841,0.00007070258],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000823641,0.0003752203,0.01146768,0.001357395,0.0002280399,0.001419552,0.001965273,0.03931934,0.1342046,0.03452744,0.008804385,0.7655075],"study_design_scores_gemma":[0.0001994549,0.0009981839,0.03015458,0.000737838,0.0006349794,0.005699398,0.001630838,0.438906,0.1707734,0.1040854,0.2458078,0.0003722274],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06887721,0.002454034,0.9066057,0.0008139491,0.0001258275,0.0003230412,0.0004405771,0.005337502,0.01502217],"genre_scores_gemma":[0.7701213,0.001830489,0.2218517,0.0004458116,0.000124282,0.0001905421,0.0004688357,0.0001243017,0.004842612],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002122943,"threshold_uncertainty_score":0.007101953,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01778563733828033,"score_gpt":0.3071358654828529,"score_spread":0.2893502281445726,"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."}}