{"id":"W1910715712","doi":"10.1109/ccece.1993.332439","title":"Approximation of spectrogrammes by cubic splines using the Kalman filter","year":2002,"lang":"en","type":"article","venue":"","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"","keywords":"Kalman filter; Spline (mechanical); Convolution (computer science); Applied mathematics; Algorithm; Mathematics; Set (abstract data type); Computer science; Artificial intelligence; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009336616,0.0004653607,0.0005317651,0.000856748,0.0003458434,0.0008515323,0.0005724746,0.0005489309,0.001861326],"category_scores_gemma":[0.003260867,0.0004810303,0.0007159963,0.001380298,0.000437156,0.0008295529,0.0005145089,0.0009334658,0.0007910704],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006706547,"about_ca_system_score_gemma":0.001274922,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02416896,"about_ca_topic_score_gemma":0.01917291,"domain_scores_codex":[0.9996079,0.0001166287,0.00002318192,0.00007422059,0.0001413108,0.00003677025],"domain_scores_gemma":[0.9993686,0.0003119123,0.00007522873,0.00006687913,0.0001608377,0.00001659937],"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":[0.00004405937,0.00001460487,0.0008677212,0.00005129067,0.00002598237,0.00002784425,0.00008757191,0.9003522,0.002458036,0.0112535,0.0007422725,0.08407503],"study_design_scores_gemma":[0.000002540472,0.000004580841,0.0001855615,0.000003965945,0.000002462216,0.00000638479,0.000005820702,0.9965811,0.0004639284,0.001834563,0.000903546,0.000005428172],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004292083,0.0000380258,0.9950252,0.00002304763,0.000008897609,0.000005304095,0.00002895064,0.0003001356,0.0002782803],"genre_scores_gemma":[0.2764264,0.0004025334,0.7189953,0.0000197203,0.00002944257,0.00008519815,0.0004215638,0.0002120221,0.003407806],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02416896,"threshold_uncertainty_score":0.0480566,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0271106609046255,"score_gpt":0.2523410436186221,"score_spread":0.2252303827139966,"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."}}