{"id":"W3157973799","doi":"","title":"Healthy Data: Policy Solutions for Big Data and AI Innovation in Health","year":2018,"lang":"en","type":"other","venue":"TSpace","topic":"Hermeneutics and Narrative Identity","field":"Arts and Humanities","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Association of Universities and Colleges of Canada","funders":"University of Toronto","keywords":"Big data; Health data; Data science; Computer science; Business; Political science; Data mining; Health care; Law","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006663838,0.0001274258,0.0002203483,0.0004537845,0.0002622755,0.000122719,0.000316947,0.00006917257,0.0006923805],"category_scores_gemma":[0.0001107093,0.0001219576,0.000007393993,0.0001180451,0.0001681322,0.0001040877,0.0003745587,0.000104737,0.0000426499],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000653484,"about_ca_system_score_gemma":0.0005438993,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.03000448,"about_ca_topic_score_gemma":0.4137004,"domain_scores_codex":[0.9989725,0.00004738968,0.0002469358,0.0003575012,0.00008872383,0.0002869591],"domain_scores_gemma":[0.9989009,0.00002028145,0.0002178046,0.0007584346,0.00006087728,0.0000416909],"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":[0.000005054803,0.00003093497,0.00001553082,0.000171282,0.00001375595,1.282652e-7,0.001596596,2.000006e-8,5.221499e-7,0.1317288,0.8643967,0.002040735],"study_design_scores_gemma":[0.0002636638,0.00007734873,0.00003334784,0.0001052649,0.000004828605,2.932152e-7,0.0005605199,0.000127575,7.699749e-8,0.001381276,0.9973323,0.0001135413],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.0001639825,0.002682531,0.001027781,0.09897136,0.003669627,0.002165352,0.01026207,0.0001928546,0.8808644],"genre_scores_gemma":[0.001007534,0.0007136412,0.0001138522,0.003301835,0.008162213,0.00002007492,0.003664605,0.0001393037,0.982877],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.383696,"threshold_uncertainty_score":0.9764548,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2851940190199785,"score_gpt":0.4311302429290137,"score_spread":0.1459362239090352,"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."}}