{"id":"W1602863930","doi":"10.1109/mwsym.1992.188252","title":"Fast nonlinear waveform estimation for large distributed networks","year":2003,"lang":"en","type":"article","venue":"","topic":"Low-power high-performance VLSI design","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Waveform; Nonlinear system; Convolution (computer science); Computer science; Algorithm; Speedup; Inversion (geology); Estimation theory; Exponential function; Mathematics; Artificial intelligence; Telecommunications; Mathematical analysis; Parallel computing; Physics; Artificial neural network","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":[0.0001806715,0.0001559968,0.0001427805,0.0000488613,0.00008051652,0.00003973792,0.00008975201,0.0001033345,0.0001280681],"category_scores_gemma":[0.00002981807,0.000137868,0.00005221298,0.0002068236,0.000008104714,0.0002498011,0.000008060067,0.000101757,0.00008006438],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008409147,"about_ca_system_score_gemma":0.00001216253,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001087903,"about_ca_topic_score_gemma":0.000007969124,"domain_scores_codex":[0.9991534,0.000006324502,0.0002132858,0.0001284052,0.00009684099,0.0004017341],"domain_scores_gemma":[0.9996266,0.00003843419,0.00002004449,0.0001977534,0.00004422736,0.00007291412],"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":[0.00001142758,0.0000597659,0.000362004,0.00009247455,0.00005758937,0.000001968341,0.0000922441,0.9656232,0.00025315,0.009616771,0.01600422,0.007825172],"study_design_scores_gemma":[0.0005871061,0.00003095969,0.0001434488,0.00001005318,0.00001219374,0.000003411347,0.00002243178,0.9648229,0.00501672,0.00008242147,0.02907829,0.0001900627],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01055533,0.00005977908,0.9842151,0.00001807137,0.0004994049,0.0003325819,0.00006009952,0.0005184828,0.003741097],"genre_scores_gemma":[0.9442197,0.00002017486,0.05477355,0.0000483592,0.0001063465,0.00006481405,0.0003756729,0.0000504514,0.0003409112],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9336644,"threshold_uncertainty_score":0.5622094,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006132603795236845,"score_gpt":0.205636140235052,"score_spread":0.1995035364398152,"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."}}