{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002580243,0.0002009714,0.000197286,0.000148594,0.001016076,0.0002997956,0.0004959349,0.00003177991,0.00003796536],"category_scores_gemma":[0.00006644792,0.0001718449,0.00001881622,0.0007323174,0.00006105073,0.0005839524,0.0005710917,0.0005250393,0.000002365939],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005335706,"about_ca_system_score_gemma":0.0001863657,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002844915,"about_ca_topic_score_gemma":0.0002362534,"domain_scores_codex":[0.998488,0.0001401337,0.0002117941,0.0004966969,0.0003470964,0.0003162892],"domain_scores_gemma":[0.9993068,0.00008415734,0.0001855395,0.0002547033,0.0000633306,0.0001054593],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007116245,0.0002801791,0.8892227,0.0006364459,0.00005971831,0.0001560553,0.01028618,0.04061665,0.001600186,0.007096112,0.00002034644,0.04995423],"study_design_scores_gemma":[0.0008841723,0.0006588848,0.02761789,0.0001133281,0.00002612873,0.0004009379,0.0003866931,0.9660715,0.000221489,0.0007527741,0.002429803,0.000436351],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1857291,0.0005957586,0.8118972,0.0004782158,0.00009480599,0.0002032064,0.00001177981,0.0003780124,0.0006118941],"genre_scores_gemma":[0.9710103,0.00002579083,0.02859536,0.0001229141,0.00002612306,0.00002716178,0.00001247797,0.00001884321,0.0001610091],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9254549,"threshold_uncertainty_score":0.7814941,"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."}}