{"id":"W4284973546","doi":"10.1016/j.cjca.2022.07.001","title":"Big Data, Big Expectations, and Big Judgements","year":2022,"lang":"en","type":"letter","venue":"Canadian Journal of Cardiology","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University; McGill University Health Centre","funders":"Fonds de Recherche du Québec - Santé","keywords":"Medicine; Big data; Data science; Data mining","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"about_ca":false,"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":[0.02073637,0.0006525857,0.00145775,0.001440856,0.007975365,0.01096311,0.002222198,0.0469454,0.009563644],"category_scores_gemma":[0.1042698,0.000760978,0.001291395,0.001716283,0.01194512,0.008746488,0.003788173,0.0604267,0.003833314],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01080863,"about_ca_system_score_gemma":0.01584244,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0264183,"about_ca_topic_score_gemma":0.0501852,"domain_scores_codex":[0.9838717,0.007378231,0.001193835,0.001358416,0.00494417,0.001253551],"domain_scores_gemma":[0.8704672,0.09855729,0.003961839,0.002604621,0.01034395,0.01406509],"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":[0.0000228062,0.00001280723,0.0005110321,0.00002394139,0.00001283499,0.0003776108,0.0002933799,0.00003681793,0.00002338358,0.008013844,0.9863672,0.004304413],"study_design_scores_gemma":[0.0001491324,0.00002383753,0.001756324,0.0006199616,0.0000283934,0.000934177,0.00292837,0.0008930793,0.00008321275,0.09030939,0.9021546,0.0001196172],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"commentary","genre_scores_codex":[0.00008124486,0.0002843936,0.00004564627,0.9958869,0.002799746,0.000002628031,0.00002587309,0.000004588656,0.0008689932],"genre_scores_gemma":[0.003575757,0.0005723334,0.0002510245,0.9742534,0.01890315,0.00002469418,0.00002472387,0.00001906356,0.002375871],"genre_candidate":"commentary","genre_consensus":"commentary","teacher_disagreement_score":0.0469454,"threshold_uncertainty_score":0.1096657,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06016978306376249,"score_gpt":0.2742516842905037,"score_spread":0.2140819012267412,"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."}}