{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006682009,0.0007854223,0.0004589976,0.002467158,0.002401307,0.01021645,0.00132406,0.003179691,0.1719635],"category_scores_gemma":[0.01562013,0.0005602468,0.0003819246,0.005005687,0.002777703,0.008480824,0.006278391,0.004237634,0.03404336],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005821726,"about_ca_system_score_gemma":0.01512479,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03378016,"about_ca_topic_score_gemma":0.05526767,"domain_scores_codex":[0.9977246,0.0007596269,0.0001087679,0.0001976436,0.0009940711,0.0002153838],"domain_scores_gemma":[0.987733,0.005409447,0.0003875463,0.001402864,0.002522154,0.002545069],"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":[0.00001188459,0.000009246472,0.0001102988,0.0001464758,0.000003381234,0.00002685131,0.0004070924,0.000215225,0.0000793461,0.05214321,0.9021791,0.0446679],"study_design_scores_gemma":[0.000006040986,0.000003178034,0.0002751637,0.0001819564,0.000001740652,0.00001506455,0.0004379026,0.0002690432,0.00009123796,0.023982,0.9747308,0.000005889483],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.001491716,0.01234858,0.02676471,0.2907034,0.009017912,0.0002672168,0.02130865,0.006203644,0.6318941],"genre_scores_gemma":[0.03520759,0.02195024,0.03451718,0.0123048,0.003357004,0.000483964,0.01929633,0.005356612,0.8675264],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.1719635,"threshold_uncertainty_score":0.5752751,"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."}}