{"id":"W3187500109","doi":"10.1002/cjs.11631","title":"Recovery of sums of sparse and dense signals by incorporating graphical structure among predictors","year":2021,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Division of Information and Intelligent Systems; Division of Mathematical Sciences; National Institute of General Medical Sciences","keywords":"Estimator; Regularization (linguistics); Computer science; Graphical model; Algorithm; SIGNAL (programming language); Sparse matrix; Sparse approximation; Compressed sensing; Artificial intelligence; Mathematics; Statistics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002262726,0.001305418,0.001053561,0.0009002185,0.0003679389,0.001012771,0.001148468,0.001101465,0.001031031],"category_scores_gemma":[0.008320385,0.0007046256,0.001023575,0.001062952,0.0014245,0.00176202,0.002057179,0.001970675,0.0003506137],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004097772,"about_ca_system_score_gemma":0.0009346045,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001655862,"about_ca_topic_score_gemma":0.002115514,"domain_scores_codex":[0.9990377,0.0004572774,0.00004005973,0.0001722528,0.0002236561,0.00006913087],"domain_scores_gemma":[0.9958935,0.002967206,0.0004344748,0.0003652054,0.0002255723,0.0001139779],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001559109,0.0000702661,0.001447711,0.0001830643,0.0001179396,0.0002225008,0.0001515878,0.8328424,0.009647306,0.07327124,0.00173238,0.0801578],"study_design_scores_gemma":[0.000006590212,0.00001771126,0.0001212616,0.000006406457,0.000007271078,0.00003087664,0.000008037197,0.986982,0.0006407349,0.01184865,0.0003220545,0.000008259099],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006194573,0.00008287128,0.9931372,0.0001111305,0.00001436275,0.00001251898,0.00002564546,0.0000910044,0.0003306938],"genre_scores_gemma":[0.4051355,0.0006089637,0.5905134,0.0002819431,0.0001437919,0.0001310373,0.0004123037,0.0001237989,0.002649296],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002262726,"threshold_uncertainty_score":0.01196659,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007944128786387587,"score_gpt":0.1839918249271909,"score_spread":0.1760476961408033,"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."}}