{"id":"W4408757431","doi":"10.1002/wics.70013","title":"Nuclear Norm Regularization","year":2025,"lang":"en","type":"review","venue":"Wiley Interdisciplinary Reviews Computational Statistics","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematics; Regularization (linguistics); Norm (philosophy); Computer science; Applied mathematics; Econometrics; Artificial intelligence; Philosophy; Epistemology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.002569302,0.001240281,0.001631118,0.001695901,0.0005303318,0.002494361,0.001564373,0.001678113,0.007187698],"category_scores_gemma":[0.005802009,0.0004870502,0.0009299997,0.002444108,0.002733148,0.00237794,0.001937996,0.002760682,0.003341395],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001031475,"about_ca_system_score_gemma":0.001625523,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00159917,"about_ca_topic_score_gemma":0.001271332,"domain_scores_codex":[0.9981433,0.0006008349,0.00009864617,0.000309579,0.0007581841,0.00008946409],"domain_scores_gemma":[0.9974782,0.001276778,0.0002206705,0.0002842426,0.0006624393,0.00007770011],"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":[0.00005887951,0.00004142011,0.000346848,0.001987882,0.0001244525,0.000180813,0.0001195852,0.04242876,0.004557082,0.6344762,0.0306299,0.285048],"study_design_scores_gemma":[0.00003590727,0.0001087144,0.000766538,0.001109684,0.00006678017,0.0007814256,0.0001244853,0.175283,0.006117494,0.529957,0.2855426,0.0001064349],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.002765229,0.05688387,0.8956012,0.00271341,0.0008916428,0.00007876247,0.0003625355,0.0004661731,0.04023719],"genre_scores_gemma":[0.222572,0.2365254,0.4837979,0.003743366,0.00360214,0.0007565893,0.002595843,0.001010474,0.04539632],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.007187698,"threshold_uncertainty_score":0.02404523,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0314365038822919,"score_gpt":0.3316734558952978,"score_spread":0.3002369520130059,"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."}}