{"id":"W2150319187","doi":"10.1016/j.asoc.2013.03.011","title":"Incremental learning of privacy-preserving Bayesian networks","year":2013,"lang":"en","type":"article","venue":"Applied Soft Computing","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":false,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal; Toronto Metropolitan University; Memorial University of Newfoundland","funders":"","keywords":"Computer science; Bayesian network; Artificial intelligence; Protocol (science); Machine learning; Data mining","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":[],"consensus_categories":[],"category_scores_codex":[0.0004547752,0.0001913644,0.0002578301,0.0000847856,0.0002563144,0.0002047926,0.001343356,0.00009330718,0.00003321774],"category_scores_gemma":[0.00004790336,0.0001962877,0.00006270122,0.0003731273,0.00004877757,0.0002590127,0.001371435,0.000401742,0.00004040796],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002988047,"about_ca_system_score_gemma":0.00003831199,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001329958,"about_ca_topic_score_gemma":8.442206e-7,"domain_scores_codex":[0.9983073,0.00006377257,0.0004358128,0.0004371681,0.0002834192,0.0004724951],"domain_scores_gemma":[0.9988192,0.0002253559,0.000231933,0.0005072799,0.00009354697,0.0001226631],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000063931,0.0001135918,0.007184748,0.00008486425,0.00007264735,0.000004551116,0.003169818,0.296322,0.01715168,0.09894249,0.0009478232,0.5759994],"study_design_scores_gemma":[0.0001862729,0.00002997477,0.00162647,0.00005352203,0.00000388492,0.000004214773,0.0000972251,0.9919445,0.001048286,0.004747165,0.00004539683,0.0002130795],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08927672,0.00006154177,0.9050792,0.0001127621,0.0001114446,0.0001748478,7.973392e-8,0.0003038231,0.004879571],"genre_scores_gemma":[0.8576431,0.000002653246,0.1420552,0.0001766054,0.00007743946,0.000007364886,0.0000019971,0.0000153552,0.00002030118],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7683663,"threshold_uncertainty_score":0.8004382,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01279108997639566,"score_gpt":0.227955091550919,"score_spread":0.2151640015745233,"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."}}