{"id":"W2128021726","doi":"10.1109/icassp.2004.1326313","title":"Joint AR parameter and order estimation in a general noise environment","year":2004,"lang":"en","type":"article","venue":"","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Autoregressive model; Noise (video); Joint (building); Computer science; Estimation theory; Noise measurement; Estimation; Algorithm; Statistics; Value noise; Mathematics; Mathematical optimization; Artificial intelligence; Noise reduction; Noise floor; Engineering","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":[0.0001402333,0.00006033164,0.00006398733,0.00007554329,0.00001921448,0.0000700773,0.00009061274,0.00003272072,0.00001253113],"category_scores_gemma":[0.00001213068,0.00005233582,0.00001093437,0.00008742356,0.00001869588,0.0002910784,0.00007696969,0.0000540778,0.00002528191],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003961676,"about_ca_system_score_gemma":0.00001709785,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007173097,"about_ca_topic_score_gemma":0.00001193975,"domain_scores_codex":[0.9994964,0.00002282057,0.0001278504,0.0001767924,0.0000866453,0.00008949701],"domain_scores_gemma":[0.9997427,0.00001027274,0.00002535862,0.0001835108,0.000006663687,0.00003149985],"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":[0.000008702242,0.0004924038,0.001576154,0.00001744533,0.00001518167,0.00002144323,0.008386412,0.1350709,0.01218104,0.7235089,0.0004551703,0.1182663],"study_design_scores_gemma":[0.0009031074,0.0001645224,0.02625338,0.00001714706,0.000002468233,0.00002266139,0.0000166234,0.79066,0.04751681,0.133114,0.001017706,0.0003116553],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2132146,0.00001119623,0.784385,0.001662623,0.000009514723,0.0001061474,9.614167e-8,0.00007736538,0.0005335035],"genre_scores_gemma":[0.4583831,0.000008115653,0.540839,0.0006701362,0.000002548991,0.00001418705,6.234889e-7,0.000002028635,0.00008022429],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.6555891,"threshold_uncertainty_score":0.2134193,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01523193332262918,"score_gpt":0.2380629380411305,"score_spread":0.2228310047185013,"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."}}