{"id":"W3112979811","doi":"10.23889/ijpds.v5i5.1529","title":"Building A Research Partnership Between Computer Scientists and Health Service Researchers for Access and Analysis of Population-Level Health Datasets","year":2020,"lang":"en","type":"article","venue":"International Journal for Population Data Science","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Vector Institute; Hospital for Sick Children","funders":"","keywords":"General partnership; Population; Timeline; Computer science; Data science; Data access; Population health; Data sharing; Knowledge management; Public relations; Political science; Medicine; Environmental health","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.2330656,0.001140013,0.001050972,0.003862617,0.0118915,0.0200299,0.007378848,0.007555575,0.01863799],"category_scores_gemma":[0.1783398,0.00178798,0.00214186,0.004632391,0.007336979,0.01765181,0.04758464,0.01373255,0.00832286],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02539073,"about_ca_system_score_gemma":0.3186268,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0975403,"about_ca_topic_score_gemma":0.1100944,"domain_scores_codex":[0.8243521,0.1110433,0.00570163,0.01185664,0.02721456,0.01983177],"domain_scores_gemma":[0.5712951,0.08025949,0.0145121,0.03568906,0.1200577,0.1781866],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0008198149,0.001736947,0.06850428,0.002428992,0.0006359741,0.001983085,0.03441037,0.004974,0.005863567,0.09935446,0.3984175,0.380871],"study_design_scores_gemma":[0.0007420342,0.001024995,0.02872861,0.003963043,0.0001984248,0.0008999446,0.0447222,0.01186232,0.004257697,0.08800036,0.8152432,0.0003572059],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.02629679,0.005582653,0.1333991,0.7898939,0.005268821,0.004023209,0.001769842,0.001933233,0.03183241],"genre_scores_gemma":[0.3082028,0.006499733,0.5191262,0.117693,0.003400953,0.007664994,0.005200513,0.001123379,0.03108828],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7669344,"threshold_uncertainty_score":0.9457666,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.9001140944178496,"score_gpt":0.7039699978539685,"score_spread":0.1961440965638811,"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."}}