{"id":"W2909641699","doi":"","title":"Doubly Sparse Regularized Regression Incorporating Graphical Structure Among Predictors","year":2018,"lang":"en","type":"dissertation","venue":"The Atrium (University of Guelph)","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Agricultural Adaptation Council; Eisai; Natural Sciences and Engineering Research Council of Canada; BioClinica; Ontario Ministry of Agriculture, Food and Rural Affairs; Bristol-Myers Squibb; Eli Lilly and Company; Genentech; IXICO; Ministry of Agriculture, Food and Rural Affairs; Alzheimer's Drug Discovery Foundation; Biogen; U.S. Department of Defense","keywords":"Regression; Graphical model; Artificial intelligence; Computer science; Pattern recognition (psychology); Mathematics; Statistics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002970037,0.0002681595,0.0003673923,0.0002750719,0.0005814384,0.00006359072,0.001665426,0.0005065295,0.0001233613],"category_scores_gemma":[0.00004067381,0.0002153454,0.0002332724,0.0006319698,0.0002465615,0.0006148894,0.0003099981,0.0005046836,0.00002429104],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003373023,"about_ca_system_score_gemma":0.0001315248,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003202814,"about_ca_topic_score_gemma":0.0005609394,"domain_scores_codex":[0.998261,0.0001889097,0.0002243417,0.0004948501,0.0005925485,0.0002383916],"domain_scores_gemma":[0.9979057,0.00007126422,0.0007741441,0.0008006594,0.0003336005,0.0001145818],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.002791686,0.0003357958,0.006441412,0.001092694,0.0008052102,0.0002534007,0.03791846,0.0001224234,0.8103715,0.009888345,0.09532578,0.03465328],"study_design_scores_gemma":[0.006841898,0.001126361,0.8098275,0.004951334,0.001186011,0.00004974876,0.02165155,0.03556112,0.03303818,0.07682616,0.00599682,0.002943337],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9928242,0.00007077726,0.004151981,0.0003521934,0.0009764497,0.0003229666,0.00002924193,0.0001438867,0.001128277],"genre_scores_gemma":[0.9884423,0.0000397993,0.008408985,0.00002263355,0.000131974,3.928814e-7,0.0004318321,0.00001629478,0.002505783],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8033861,"threshold_uncertainty_score":0.8781531,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0114144251968611,"score_gpt":0.2137305148664629,"score_spread":0.2023160896696018,"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."}}