{"id":"W4293243882","doi":"10.23889/ijpds.v7i3.2026","title":"Federated Learning for cross-jurisdictional analyses: A case study.","year":2022,"lang":"en","type":"article","venue":"International Journal for Population Data Science","topic":"Ethics in Clinical Research","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"","keywords":"Computer science; Software deployment; Leverage (statistics); Data sharing; Federated learning; Identification (biology); Machine learning; Artificial intelligence; Data science","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":[],"consensus_categories":[],"category_scores_codex":[0.01943921,0.0004068891,0.0003300498,0.001792253,0.004665988,0.003362681,0.002676914,0.002239355,0.002125439],"category_scores_gemma":[0.03077955,0.0002663862,0.0007427315,0.003772124,0.002150622,0.003488374,0.004466181,0.00224599,0.0005183803],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.008188498,"about_ca_system_score_gemma":0.01095206,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1544138,"about_ca_topic_score_gemma":0.1652095,"domain_scores_codex":[0.9884915,0.006342958,0.0004883609,0.001645307,0.002280455,0.0007513576],"domain_scores_gemma":[0.9645474,0.01686634,0.001905451,0.006915624,0.008038461,0.001726722],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"case_report","study_design_scores_codex":[0.001005068,0.00165515,0.2064923,0.0006601158,0.0006312267,0.01476333,0.02270515,0.09541059,0.006717921,0.1088929,0.06890544,0.4721608],"study_design_scores_gemma":[0.0002581845,0.0004707715,0.079124,0.0003325686,0.0002788633,0.006153899,0.03686073,0.4896359,0.02510972,0.09031719,0.2711719,0.0002862411],"study_design_candidate":"case_report","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6052572,0.001376137,0.3187416,0.02753291,0.0003502545,0.001265579,0.004122409,0.005467698,0.03588615],"genre_scores_gemma":[0.8707505,0.0002311356,0.1189366,0.001756372,0.00004086495,0.0001790205,0.002334035,0.0002274253,0.005543976],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1544138,"threshold_uncertainty_score":0.3070301,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.7203096947109929,"score_gpt":0.7122980366271027,"score_spread":0.00801165808389026,"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."}}