{"id":"W4247533534","doi":"10.1109/dac.2005.193932","title":"A non-parametric approach for dynamic range estimation of nonlinear systems","year":2005,"lang":"en","type":"article","venue":"Proceedings. 42nd Design Automation Conference, 2005.","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Datapath; Nonlinear system; Algorithm; Gaussian; Dynamic range; Parametric statistics; Range (aeronautics); Orthonormal basis; High dynamic range; Independent component analysis; Gaussian process; Mathematical optimization; Mathematics; Artificial intelligence; Statistics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00151661,0.0002706688,0.0004110138,0.0008084688,0.0001348209,0.0005082638,0.0007347475,0.0002165635,0.000008120855],"category_scores_gemma":[0.00019072,0.000264094,0.00009677424,0.0009592274,0.00006017622,0.001757238,0.0000686545,0.000155122,0.00002483549],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001436007,"about_ca_system_score_gemma":0.0002703269,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007543865,"about_ca_topic_score_gemma":6.148862e-7,"domain_scores_codex":[0.9977861,0.00003906914,0.000768864,0.0005186177,0.0005292325,0.0003581167],"domain_scores_gemma":[0.9979907,0.0001210716,0.0006680954,0.000268167,0.0008382338,0.0001137149],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001928899,0.001833728,0.0002633661,0.00216679,0.0002254315,8.781826e-7,0.009322869,0.1888185,0.01063679,0.5559953,0.016893,0.2136505],"study_design_scores_gemma":[0.0005998461,0.0001613111,0.0001918443,0.00006416986,0.00002056213,0.00001191127,0.00006259412,0.9906709,0.006046,0.001153212,0.000740113,0.0002775534],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002642433,0.00008551947,0.9902028,0.0002353514,0.00007941841,0.002079693,0.000008990046,0.0008249484,0.00384089],"genre_scores_gemma":[0.4830773,0.00001298507,0.5162191,0.00005121413,0.00003158869,0.0003284755,0.00001890301,0.00001412506,0.0002463111],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8018524,"threshold_uncertainty_score":0.9999811,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03145764093773942,"score_gpt":0.2803036523230371,"score_spread":0.2488460113852976,"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."}}