{"id":"W3127643160","doi":"10.3138/jmvfh-2020-0035","title":"Data safe haven for military, Veteran, and family health research","year":2021,"lang":"en","type":"article","venue":"Journal of Military Veteran and Family Health","topic":"Ethics in Clinical Research","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"IBM (Canada); Queen's University","funders":"","keywords":"Big data; Health care; Safe haven; Analytics; Data science; Computer science; Haven; Computer security; Data access; Database; Political science","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":true,"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":[0.1302832,0.0006237021,0.001341226,0.005529081,0.008530971,0.01343744,0.005355714,0.007212505,0.0712266],"category_scores_gemma":[0.2767617,0.001401546,0.001793153,0.004098867,0.00718764,0.01541283,0.0271139,0.01133407,0.02222786],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.008732983,"about_ca_system_score_gemma":0.07162569,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005360745,"about_ca_topic_score_gemma":0.01089773,"domain_scores_codex":[0.8973544,0.06421839,0.009522371,0.003818559,0.02066028,0.004425971],"domain_scores_gemma":[0.5401511,0.1718447,0.02532929,0.1007128,0.08731467,0.07464745],"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":[0.0002991775,0.0001706652,0.006418776,0.0008650584,0.00006848342,0.0004816768,0.002404475,0.0003168141,0.0004248273,0.1043095,0.670241,0.2139995],"study_design_scores_gemma":[0.0001038235,0.0001105799,0.003058225,0.003478114,0.00002645093,0.0005102736,0.00169992,0.0005491089,0.0006281303,0.07787588,0.9118798,0.00007964531],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.007780956,0.01337949,0.06881917,0.7509815,0.01421242,0.00229202,0.00718217,0.00354861,0.1318037],"genre_scores_gemma":[0.1725817,0.02381821,0.3719152,0.2601844,0.01698168,0.01219552,0.01888433,0.003053702,0.1203853],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.1302832,"threshold_uncertainty_score":0.6890118,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.7503043763753028,"score_gpt":0.625082303349198,"score_spread":0.1252220730261048,"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."}}