{"id":"W2728522947","doi":"10.1097/ede.0000000000000711","title":"Big Data and Population Health","year":2017,"lang":"en","type":"article","venue":"Epidemiology","topic":"Health disparities and outcomes","field":"Social Sciences","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute for Clinical Evaluative Sciences; Public Health Ontario; University of Toronto","funders":"","keywords":"Big data; Data science; Population health; Poverty; Socioeconomic status; Population; Macro; Social determinants of health; Public health; Political science; Environmental health; Computer science; Medicine; Data mining","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":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.00525212,0.00003776456,0.0002136919,0.00001485637,0.001562827,0.00001892113,0.000336732,0.00006975896,0.00002638326],"category_scores_gemma":[0.008123138,0.00003306488,0.00001059005,0.00001122354,0.0001991752,0.0001279207,0.0001374278,0.00007024218,0.00001129754],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003097962,"about_ca_system_score_gemma":0.0001188517,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.1904845,"about_ca_topic_score_gemma":0.06903031,"domain_scores_codex":[0.9986466,0.0005893593,0.0002058892,0.0001809078,0.00004079695,0.0003364526],"domain_scores_gemma":[0.9982401,0.0008910953,0.0001962034,0.0004998833,0.000009133838,0.0001635598],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000001162153,0.000001916471,0.8201678,0.00001131017,0.000001812992,1.987442e-7,0.0002670655,1.206733e-7,9.635749e-9,0.09230204,0.01418667,0.07305988],"study_design_scores_gemma":[0.00003992898,0.000005117161,0.7020088,0.000006258746,9.277324e-7,2.492813e-7,0.000136566,0.00004392885,6.394029e-9,0.01371099,0.2840234,0.00002374568],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.1365466,0.00256167,0.0005632046,0.8489473,0.002309857,0.0002202518,0.00003050015,0.00005062165,0.008770007],"genre_scores_gemma":[0.9667535,0.002195925,0.000842749,0.028477,0.001010098,0.000002361135,0.0000314608,0.000003323639,0.0006836249],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8302069,"threshold_uncertainty_score":0.999737,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4423928385943427,"score_gpt":0.5321099536020673,"score_spread":0.08971711500772456,"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."}}