{"id":"W4377230041","doi":"10.18280/ts.400208","title":"Premature Ventricular Contraction Detection Based on Chebyshev Polynomials and K Nearest Neighbours Classifier","year":2023,"lang":"en","type":"article","venue":"Traitement du signal","topic":"ECG Monitoring and Analysis","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Chebyshev polynomials; Chebyshev filter; Contraction (grammar); Classifier (UML); Mathematics; Artificial intelligence; Pattern recognition (psychology); Computer science; Internal medicine; Cardiology; Medicine; Mathematical analysis","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002961966,0.0001369753,0.0002156156,0.0002215106,0.000134565,0.00004566144,0.000031178,0.0001075681,0.0001425407],"category_scores_gemma":[0.00004865828,0.0001149142,0.0001092358,0.0003285257,0.00002473665,0.00005568986,0.000007482741,0.0001909175,0.00004486101],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007856744,"about_ca_system_score_gemma":0.00002779625,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002730992,"about_ca_topic_score_gemma":0.000005268813,"domain_scores_codex":[0.9989637,0.00005273967,0.0001987423,0.0002494467,0.0003196063,0.0002157864],"domain_scores_gemma":[0.9995286,0.00008835202,0.00007726285,0.0001288449,0.00004809245,0.0001288048],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.002888907,0.001324092,0.1296924,0.0006256779,0.001008903,0.0009746685,0.0005130143,0.009822349,0.6486607,0.00008326291,0.006901263,0.1975048],"study_design_scores_gemma":[0.005265444,0.001147786,0.6957939,0.0004098208,0.0008267775,0.00003811478,0.0003989571,0.1935316,0.09461702,0.00004461037,0.007545217,0.0003808346],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9960614,0.00007344028,0.001240561,0.001478862,0.0002257976,0.0002937451,0.000008289491,0.0001827312,0.0004351977],"genre_scores_gemma":[0.9985564,0.00001870635,0.0000586466,0.0002257053,0.0006323976,0.00003276132,0.00004499436,0.00001813886,0.0004121894],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5661015,"threshold_uncertainty_score":0.4686066,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01654442543743077,"score_gpt":0.2572584165913794,"score_spread":0.2407139911539487,"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."}}