{"id":"W2963763058","doi":"","title":"Convex-constrained Sparse Additive Modeling and Its Extensions.","year":2017,"lang":"en","type":"article","venue":"Uncertainty in Artificial Intelligence","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Overfitting; Convexity; Additive model; Mathematical optimization; Mathematics; Regularization (linguistics); A priori and a posteriori; Computer science; Algorithm; Convex optimization; Regular polygon; Convex function; Artificial intelligence; Machine learning; Artificial neural network","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0008115286,0.000205846,0.0003731609,0.0000861227,0.0003698191,0.0001546662,0.0003284412,0.000119779,0.0003951579],"category_scores_gemma":[0.01477071,0.000184931,0.00004809311,0.00007667358,0.0003515083,0.0001416532,0.0001523379,0.0003059651,0.00005814388],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003978295,"about_ca_system_score_gemma":0.00006980245,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002804301,"about_ca_topic_score_gemma":0.0003312253,"domain_scores_codex":[0.9983157,0.0001186361,0.0005615823,0.0004205128,0.000206399,0.0003772075],"domain_scores_gemma":[0.9975196,0.001479436,0.0001771026,0.0004410892,0.0002350467,0.0001476881],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00005364634,0.00006699475,0.00002883348,0.00002042306,0.000008601975,0.00003733251,0.0005787804,0.0006252253,0.0005990269,0.8667287,0.00002001999,0.1312324],"study_design_scores_gemma":[0.00002898513,0.00003374057,0.0000342258,0.0001048433,0.000008863623,0.000004590449,0.0005530857,0.4741134,0.002105374,0.5228595,0.00001455994,0.0001388428],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4358971,0.0001055543,0.5547498,0.001089322,0.000549273,0.0006609493,0.0001334661,0.0000869291,0.006727575],"genre_scores_gemma":[0.9629824,0.00007852727,0.03665048,0.0001075347,0.00007965365,0.00002868975,0.000002367398,0.00001515452,0.00005524433],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5270852,"threshold_uncertainty_score":0.9935283,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2580744315264659,"score_gpt":0.4199282445267047,"score_spread":0.1618538130002388,"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."}}