{"id":"W1945859329","doi":"10.1109/icassp.1995.480581","title":"A self-calibration algorithm for cyclostationary signals and its uniqueness analysis","year":2002,"lang":"en","type":"article","venue":"","topic":"Structural Health Monitoring Techniques","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Cyclostationary process; Uniqueness; Algorithm; Consistency (knowledge bases); Calibration; Nonlinear system; Computer science; Optimization algorithm; Optimization problem; Noise (video); Mathematical optimization; Mathematics; Statistics; Artificial intelligence; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001093417,0.0005494378,0.0005849723,0.0007092464,0.0005100768,0.0006837471,0.000850819,0.0009877397,0.002884258],"category_scores_gemma":[0.003041396,0.0004249291,0.0005034702,0.000838465,0.0007264886,0.001230931,0.001251693,0.00117456,0.001369127],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003575564,"about_ca_system_score_gemma":0.0007399085,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006597897,"about_ca_topic_score_gemma":0.0006679033,"domain_scores_codex":[0.9995115,0.0001236869,0.00002588995,0.00009630245,0.0002117653,0.00003097912],"domain_scores_gemma":[0.9991793,0.0002919092,0.00009663378,0.0001282361,0.0002728111,0.0000311922],"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":[0.0001184967,0.00008396986,0.0007104385,0.0001457504,0.00007961685,0.00008796684,0.0001909595,0.2097782,0.0434369,0.1046381,0.003773922,0.6369555],"study_design_scores_gemma":[0.00001374716,0.00006022802,0.0002565373,0.00001402415,0.000009266147,0.0001766266,0.00001351625,0.9734936,0.01047262,0.01037299,0.005088259,0.00002856225],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001615182,0.00005293656,0.9977378,0.00003286028,0.00001459971,0.00001183118,0.00000588844,0.0001069985,0.0004219533],"genre_scores_gemma":[0.065405,0.000167911,0.9310714,0.00006698303,0.00004618341,0.0001648657,0.00007127321,0.0001179773,0.002888366],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002884258,"threshold_uncertainty_score":0.00964886,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02211261549574399,"score_gpt":0.2754218781731883,"score_spread":0.2533092626774443,"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."}}