{"id":"W2606810975","doi":"10.23889/ijpds.v1i1.364","title":"Big Data, Big Responsibility! Building best-practice privacy strategies into a large-scale neuroinformatics platform","year":2017,"lang":"en","type":"article","venue":"International Journal for Population Data Science","topic":"Health, Environment, Cognitive Aging","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Brain Institute","funders":"","keywords":"Neuroinformatics; Computer science; Data sharing; Big data; Data science; Health informatics; Data governance; Informatics; Variety (cybernetics); Best practice; Analytics; Computer security; Health care; Medicine; Engineering; Data mining; Political science; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"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":["metaresearch","sts","scholarly_communication","open_science"],"consensus_categories":["scholarly_communication"],"category_scores_codex":[0.007537019,0.0002009926,0.0001697032,0.0002162044,0.002752228,0.002440544,0.007983837,0.00006292655,0.00008869619],"category_scores_gemma":[0.01121151,0.0001891784,0.00004162278,0.0001897632,0.0005648396,0.02558124,0.005730155,0.0004257262,0.0001175289],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000578273,"about_ca_system_score_gemma":0.0002964063,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001135689,"about_ca_topic_score_gemma":0.0008511676,"domain_scores_codex":[0.995815,0.00007093343,0.0007711406,0.0008795412,0.001926108,0.0005372433],"domain_scores_gemma":[0.9953455,0.0003802791,0.001106712,0.002715535,0.0001534005,0.0002985947],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005798072,0.0007665328,0.1219136,0.00004642407,0.00008151111,0.00008898682,0.004003596,0.006378578,0.008997769,0.00332207,0.002288591,0.8515325],"study_design_scores_gemma":[0.001733706,0.0002117462,0.2921949,0.0001803551,0.00007442333,0.0004995039,0.0031176,0.3943774,0.0003453407,0.01439162,0.2921999,0.0006736172],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7979256,0.00001975518,0.1898052,0.004299649,0.004407395,0.0005711906,0.0006938466,0.00003635581,0.002241014],"genre_scores_gemma":[0.9253259,0.0001189515,0.07292099,0.0006187819,0.0005874123,0.000006295215,0.0003110825,0.00001894612,0.00009169585],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8508589,"threshold_uncertainty_score":0.998595,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1355927240826561,"score_gpt":0.427343584362575,"score_spread":0.2917508602799189,"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."}}