{"id":"W2614092723","doi":"10.1016/j.annepidem.2017.05.002","title":"Ethics, big data and computing in epidemiology and public health","year":2017,"lang":"en","type":"article","venue":"Annals of Epidemiology","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":88,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University; McMaster University","funders":"","keywords":"Big data; Epidemiology; Medicine; Harm; Public health; Confidentiality; Research ethics; Engineering ethics; Information ethics; Public relations; Political science; Law; Computer science; Pathology; Data mining; Engineering","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":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.03763669,0.0001753471,0.001716348,0.0001619881,0.0001933804,0.000005887101,0.0005049948,0.0002684503,0.000009809687],"category_scores_gemma":[0.2107376,0.0001527581,0.00004682893,0.00005834137,0.001409294,0.0001417544,0.001147454,0.0006308682,0.000005089561],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001142594,"about_ca_system_score_gemma":0.0002958837,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0028554,"about_ca_topic_score_gemma":0.001174965,"domain_scores_codex":[0.9932554,0.003522448,0.001333194,0.0008447552,0.00007725723,0.0009669096],"domain_scores_gemma":[0.983811,0.01198899,0.001209256,0.002256114,0.0001506624,0.0005840366],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00005283098,0.00005396607,0.8769506,0.0002302876,0.0000536711,0.0000107547,0.00009498774,0.000001366437,0.000005520783,0.00404952,0.01103937,0.1074571],"study_design_scores_gemma":[0.0006218879,0.0001956008,0.946377,0.0001666745,0.000006851332,0.00004808408,0.00005181499,0.002499879,0.000001145886,0.005279475,0.04464921,0.0001023653],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5719091,0.02420349,0.00174019,0.4004218,0.0003257281,0.0003482983,0.0004229982,0.00004083529,0.0005875179],"genre_scores_gemma":[0.945229,0.01314776,0.004079991,0.03685893,0.0002210886,0.000002638236,0.0004277414,0.00001508836,0.00001778448],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3733199,"threshold_uncertainty_score":0.9909555,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.6947970834722285,"score_gpt":0.5445765864036305,"score_spread":0.150220497068598,"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."}}