{"id":"W4220723969","doi":"10.3390/s22072467","title":"Structural Anomalies Detection from Electrocardiogram (ECG) with Spectrogram and Handcrafted Features","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"ECG Monitoring and Analysis","field":"Medicine","cited_by":43,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Spectrogram; Computer science; Artificial intelligence; Pattern recognition (psychology); Speech recognition","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":[0.00005014193,0.000123816,0.0002248655,0.0001217224,0.0002695533,0.00003449609,0.00003234869,0.00003198672,0.00003405337],"category_scores_gemma":[0.00001329918,0.000096754,0.00007917418,0.000322297,0.00004803816,0.00002207817,0.00002451147,0.000276326,0.000001220666],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005189028,"about_ca_system_score_gemma":0.00001442821,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006370472,"about_ca_topic_score_gemma":0.00012468,"domain_scores_codex":[0.9991639,0.0000555611,0.00008966061,0.0002502888,0.0002423107,0.0001982702],"domain_scores_gemma":[0.9996571,0.00002903,0.00004183395,0.0001656199,0.00002909842,0.00007733003],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002571881,0.00009865145,0.7446206,0.00004576128,0.002400951,0.0005224241,0.001638034,0.001796409,0.1026307,0.00001670285,0.0002388553,0.1434191],"study_design_scores_gemma":[0.003717842,0.003763745,0.8784174,0.00004565317,0.001777555,0.001220446,0.0057917,0.01171707,0.08771923,0.0002507062,0.004921018,0.0006576029],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.998518,0.0006975861,0.00002045731,0.0002318663,0.00008413325,0.0001041632,0.000009191671,0.000134356,0.0002002571],"genre_scores_gemma":[0.9980347,0.00002949785,0.000855238,0.00004600508,0.0002672027,0.00001335127,0.00003978969,0.00001896206,0.0006953048],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1427615,"threshold_uncertainty_score":0.3945514,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004517238409663837,"score_gpt":0.2114838822746569,"score_spread":0.2069666438649931,"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."}}