{"id":"W4280576960","doi":"10.1007/s10994-022-06172-1","title":"MAGMA: inference and prediction using multi-task Gaussian processes with common mean","year":2022,"lang":"en","type":"article","venue":"Machine Learning","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Army Research Laboratory; Army Research Office; Engineering and Physical Sciences Research Council; CHIST-ERA; Agence Nationale de la Recherche","keywords":"Gaussian process; Computer science; Task (project management); Inference; Computation; Process (computing); Gaussian; Machine learning; Data mining; Artificial intelligence; Algorithm","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004167861,0.001082954,0.002309917,0.001037374,0.0007219638,0.001739626,0.00281038,0.002255271,0.0020015],"category_scores_gemma":[0.01339745,0.0008941951,0.001559706,0.001519331,0.001460768,0.002081755,0.002305374,0.002989703,0.0004739584],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001133341,"about_ca_system_score_gemma":0.001946774,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01365634,"about_ca_topic_score_gemma":0.009160477,"domain_scores_codex":[0.9986659,0.0005562813,0.00006542144,0.0003385895,0.0002274075,0.0001463119],"domain_scores_gemma":[0.9947143,0.003965436,0.0003275272,0.0003415717,0.0005204508,0.0001306853],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009639391,0.00003385303,0.0006336977,0.00004621111,0.00007306365,0.00005133968,0.00004825566,0.9403148,0.0006153694,0.01656316,0.0008435572,0.04068037],"study_design_scores_gemma":[0.000004723952,0.000008866454,0.00004904258,0.000003044479,0.000004573689,0.000005058587,0.000002225941,0.9941449,0.0001364646,0.005499799,0.0001377792,0.000003634208],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006995701,0.0001791108,0.9919007,0.0002087854,0.00003677603,0.00002224296,0.00004351398,0.0002788816,0.0003342981],"genre_scores_gemma":[0.659907,0.0004341623,0.3352272,0.000364222,0.0002321345,0.0002215189,0.0003667239,0.0001683862,0.003078741],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01365634,"threshold_uncertainty_score":0.02715373,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01620512122267326,"score_gpt":0.2495476373542071,"score_spread":0.2333425161315338,"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."}}