{"id":"W4211190149","doi":"10.1016/j.jbi.2022.104008","title":"Privacy preserving collaborative learning of generalized linear mixed model","year":2022,"lang":"en","type":"article","venue":"Journal of Biomedical Informatics","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Differential privacy; Computer science; Homomorphic encryption; Usability; Encryption; Class (philosophy); Generalized linear model; Secure multi-party computation; Computation; Theoretical computer science; Data mining; Machine learning; Artificial intelligence; Computer security; Algorithm; Human–computer interaction","routes":{"ca_aff":true,"ca_fund":true,"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","open_science"],"consensus_categories":["open_science"],"category_scores_codex":[0.002077722,0.0001442508,0.0004241992,0.0005039623,0.0002066459,0.0000649931,0.01822805,0.00009841465,0.0000385595],"category_scores_gemma":[0.01598773,0.000121306,0.0001117802,0.001409914,0.0001765261,0.001213889,0.05283551,0.0009876293,0.000002332151],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000143348,"about_ca_system_score_gemma":0.0005729593,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002829181,"about_ca_topic_score_gemma":1.2041e-7,"domain_scores_codex":[0.9965229,0.0001686005,0.001466946,0.00009586656,0.001448319,0.0002973885],"domain_scores_gemma":[0.9956213,0.0002733296,0.001697995,0.001841993,0.0004143624,0.0001510534],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002015027,0.0008614606,0.0004763166,0.0005263628,0.0005620201,0.0001191811,0.01667613,0.1123021,0.006113012,0.008420394,0.8057684,0.04797311],"study_design_scores_gemma":[0.0007801405,0.0004448954,0.00001644734,0.00004602581,0.00001331031,0.00008121519,0.0008918761,0.959002,0.001517701,0.0197457,0.01733809,0.0001226335],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.09298367,0.0001811556,0.8984511,0.007475615,0.000448033,0.0001241891,0.00002860636,0.0000952402,0.0002124232],"genre_scores_gemma":[0.1333809,0.0001321564,0.866196,0.0001913966,0.00004392381,0.000005527887,0.00001004735,0.000009187825,0.00003089876],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8466998,"threshold_uncertainty_score":0.992301,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03027644141557115,"score_gpt":0.2919081236792762,"score_spread":0.261631682263705,"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."}}