{"id":"W2908143289","doi":"10.1007/s13042-018-00904-3","title":"Big data aggregation in the case of heterogeneity: a feasibility study for digital health","year":2019,"lang":"en","type":"article","venue":"International Journal of Machine Learning and Cybernetics","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Missing data; Sensor fusion; Big data; Wearable computer; Key (lock); Machine learning; Wearable technology; Data mining; Artificial intelligence; Embedded system","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.001581649,0.00006629737,0.0001430968,0.0001028906,0.00003971333,0.0001627895,0.0007372638,0.0000169511,2.982606e-7],"category_scores_gemma":[0.0002526668,0.00004737945,0.00003669047,0.00008513807,0.00001600694,0.0002050994,0.0002637817,0.0002148261,4.776713e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002634604,"about_ca_system_score_gemma":0.00006305574,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001127997,"about_ca_topic_score_gemma":0.00005002219,"domain_scores_codex":[0.9989938,0.0001391205,0.000387965,0.0001448521,0.0002423958,0.00009183567],"domain_scores_gemma":[0.9988812,0.0003286182,0.0004044831,0.0001865946,0.000170299,0.00002885734],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005679622,0.0003646073,0.5086059,0.00002004093,0.00006120108,0.0001049398,0.004067181,0.0004821887,0.000009801459,0.0001611365,0.00007971708,0.4859865],"study_design_scores_gemma":[0.006759303,0.004811333,0.1478134,0.0003534844,0.00003363289,0.007062515,0.002127429,0.8149388,0.00005930843,0.003526243,0.01216081,0.0003537069],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9762208,0.0003665063,0.0208587,0.0008689705,0.001486469,0.0001532961,0.000002849357,0.000004350629,0.00003808],"genre_scores_gemma":[0.9982495,0.00002064265,0.001279476,0.00006266852,0.0003621606,3.748137e-7,0.00000591953,0.000003644604,0.00001564162],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8144566,"threshold_uncertainty_score":0.1932078,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05559932104041591,"score_gpt":0.3503143024259312,"score_spread":0.2947149813855153,"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."}}