{"id":"W2136676913","doi":"10.1109/dcc.2009.69","title":"Model-Guided Adaptive Recovery of Compressive Sensing","year":2009,"lang":"en","type":"article","venue":"","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":52,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Compressed sensing; Signal recovery; SIGNAL (programming language); Computer science; Autoregressive model; Signal reconstruction; Wavelet; Piecewise; Set (abstract data type); Process (computing); Iterative reconstruction; Algorithm; Artificial intelligence; Signal processing; Computer vision; Mathematics; Telecommunications","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":[0.00003266917,0.0001201459,0.00019264,0.00007662064,0.0000206124,0.000009958081,0.00007904609,0.00006245982,0.000008501747],"category_scores_gemma":[0.000007161455,0.0001156626,0.00006265691,0.00008646243,0.00002050868,0.00009331303,0.00001503635,0.00008914367,0.000003318337],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002359223,"about_ca_system_score_gemma":0.000008864753,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001472874,"about_ca_topic_score_gemma":0.000002445351,"domain_scores_codex":[0.9994439,0.00001095184,0.0001854819,0.0001091628,0.000100444,0.0001500413],"domain_scores_gemma":[0.9996125,0.00002618837,0.00003354674,0.000215698,0.00007755929,0.00003456123],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002186323,0.0000187574,0.000004000642,0.000004982742,0.00004499836,0.00001320652,0.0001439941,0.6947997,0.2293223,0.00264225,0.02275314,0.05023086],"study_design_scores_gemma":[0.00007608907,0.00003920572,0.00005132844,0.00005238264,0.000008397918,0.000007538827,0.00002500195,0.6999364,0.2893394,0.01030918,0.00004460391,0.0001104858],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1975787,0.0002017604,0.6610284,0.00003844215,0.00009190523,0.0001372538,0.000004053207,0.001080382,0.1398391],"genre_scores_gemma":[0.9274692,0.00003867979,0.07223,0.000122071,0.0000257788,3.102652e-7,0.000001750903,0.00001344986,0.00009878554],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7298905,"threshold_uncertainty_score":0.4716582,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03953904448488921,"score_gpt":0.2473233525812135,"score_spread":0.2077843080963243,"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."}}