{"id":"W1573858933","doi":"10.1109/cca.1995.555743","title":"An AI based frequency weighted least-squares filter","year":2002,"lang":"en","type":"article","venue":"","topic":"Advanced Adaptive Filtering Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"","keywords":"A priori and a posteriori; Filter (signal processing); Weighting; SIGNAL (programming language); Algorithm; Noise (video); Signal processing; Computer science; Signal transfer function; Noise measurement; Heuristics; Mathematics; Signal-to-noise ratio (imaging); Artificial intelligence; Mathematical optimization; Statistics; Analog signal; Noise reduction; Digital signal processing; Acoustics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007697685,0.0006683532,0.0007771934,0.0005833936,0.000499581,0.0008086637,0.001132039,0.001400389,0.002346421],"category_scores_gemma":[0.001850738,0.000306803,0.0005354545,0.0008613803,0.0004828122,0.0009722303,0.0005094264,0.001100007,0.001370398],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005965195,"about_ca_system_score_gemma":0.0008549738,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002697824,"about_ca_topic_score_gemma":0.003019425,"domain_scores_codex":[0.9993284,0.000101447,0.0000328387,0.000154377,0.0003424434,0.00004043146],"domain_scores_gemma":[0.9995404,0.0001498837,0.00004463056,0.00004585324,0.0002041673,0.00001509238],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002389715,0.0001244382,0.0005374507,0.0001761145,0.0001141882,0.00007668829,0.0001196945,0.2039354,0.07005299,0.02442346,0.003406487,0.6967941],"study_design_scores_gemma":[0.00001860885,0.00006582114,0.0002271485,0.000008931255,0.00002020124,0.00006478399,0.000008217648,0.9828624,0.009444218,0.002610513,0.004651191,0.00001793004],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001485763,0.00006402395,0.9975526,0.00004218395,0.00003530845,0.00001432789,0.00001022458,0.0002474194,0.0005482063],"genre_scores_gemma":[0.07982115,0.0001838685,0.9146385,0.0001350508,0.00006820534,0.00009933761,0.0001082737,0.00006553641,0.004880123],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002697824,"threshold_uncertainty_score":0.007849574,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01763749790255321,"score_gpt":0.2331919994109463,"score_spread":0.2155545015083931,"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."}}