{"id":"W2337742813","doi":"","title":"Towards an Algorithmic Theory of Compressed Sensing","year":2005,"lang":"en","type":"article","venue":"","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"Bell (Canada)","funders":"","keywords":"Compressed sensing; Orthonormal basis; Signal reconstruction; SIGNAL (programming language); Computer science; Class (philosophy); Series (stratigraphy); Algorithm; Perspective (graphical); Signal processing; Basis (linear algebra); Artificial intelligence; Theoretical computer science; 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":[0.00008179688,0.00009545442,0.0001353542,0.00006028687,0.00001836783,0.00001208633,0.00009364478,0.00004905442,0.00006892639],"category_scores_gemma":[0.000004333059,0.0000883741,0.00003557557,0.00006147323,0.00003122482,0.0001014244,0.00001962634,0.00007304925,0.000008019546],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001498761,"about_ca_system_score_gemma":0.000005972205,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002017109,"about_ca_topic_score_gemma":0.000006929138,"domain_scores_codex":[0.9995324,0.00002499801,0.0001394524,0.00008812385,0.0000859764,0.0001290226],"domain_scores_gemma":[0.9996436,0.00002179734,0.00001613065,0.0002401389,0.00003987693,0.00003841066],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0000103796,0.0000332105,0.00001050648,0.00001246915,0.00004732257,0.000005939819,0.0003735106,0.018289,0.2505493,0.00636042,0.001877381,0.7224306],"study_design_scores_gemma":[0.0000933948,0.00001880195,0.0001496075,0.00002122203,0.000008196215,0.000008990514,0.00005032402,0.3601125,0.6343087,0.002741175,0.002372273,0.0001148929],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2450888,0.0002955729,0.6831964,0.00003854309,0.0001253344,0.0001178923,0.000003246344,0.001842856,0.06929137],"genre_scores_gemma":[0.8945809,0.00001859875,0.1051663,0.00005963506,0.00009904721,4.583245e-7,0.000002399909,0.00002079249,0.00005186231],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7223157,"threshold_uncertainty_score":0.3603791,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0180998166060453,"score_gpt":0.2397820733259911,"score_spread":0.2216822567199458,"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."}}