{"id":"W4249812721","doi":"10.31525/ct1-nct03988894","title":"mDASHNa-CC APP to Support a Healthy Diet and Hypertension Control for Chinese Canadian Seniors","year":2019,"lang":"en","type":"article","venue":"Case Medical Research","topic":"Nutrition, Genetics, and Disease","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Gerontology; Control (management); Medicine; Psychology; Computer science; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001038028,0.0001164966,0.000176224,0.0001331896,0.0001784202,0.00002814914,0.0001368553,0.0002049805,0.0001384521],"category_scores_gemma":[0.001979975,0.0000979292,0.00005875577,0.0001201658,0.000139786,0.000002370903,0.0000800349,0.0001584689,0.00004849615],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003389358,"about_ca_system_score_gemma":0.001096001,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.006647244,"about_ca_topic_score_gemma":0.03264225,"domain_scores_codex":[0.9982267,0.0001275848,0.0001745675,0.0004379112,0.0004205555,0.0006126633],"domain_scores_gemma":[0.9969926,0.0001273171,0.00001393405,0.0003248096,0.0003213459,0.00221996],"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":[0.01700167,0.002784304,0.1217642,0.001937939,0.0002928988,0.0102044,0.0006549958,0.00001044882,0.08962168,0.0003480546,0.6384987,0.1168807],"study_design_scores_gemma":[0.02281577,0.01138851,0.009051843,0.00008378432,0.00003282894,0.001852947,0.000665804,0.001305897,0.001251667,0.00040362,0.9505113,0.0006360176],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9922152,0.000702079,0.0001684518,0.005354913,0.0001569111,0.0009279427,0.0002073377,0.000006604789,0.000260521],"genre_scores_gemma":[0.9944149,0.000211395,0.00009782496,0.003985244,0.0004721736,0.0001163392,0.0001856777,0.00002265304,0.0004937966],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3120126,"threshold_uncertainty_score":0.9999676,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.023624622045248,"score_gpt":0.3600427829329949,"score_spread":0.3364181608877469,"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."}}