{"id":"W2522601250","doi":"10.3390/bioengineering3040022","title":"Eventogram: A Visual Representation of Main Events in Biomedical Signals","year":2016,"lang":"en","type":"article","venue":"Bioengineering","topic":"ECG Monitoring and Analysis","field":"Medicine","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"BC Children's Hospital; Children's Hospital Foundation","keywords":"Visualization; Computer science; Spectrogram; Artificial intelligence; Wavelet; SIGNAL (programming language); Photoplethysmogram; Pattern recognition (psychology); Event (particle physics); Signal processing; Representation (politics); Duration (music); Waveform; Sensitivity (control systems); Data mining; Computer vision; Speech recognition; Digital signal processing; Electronic engineering; Engineering; Acoustics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001496569,0.00006760837,0.000189061,0.0002594703,0.000006282669,0.000001239492,0.00003678217,0.0000490937,0.0000275772],"category_scores_gemma":[0.000141598,0.0000475341,0.00007479064,0.0003898008,0.00001671098,0.00003496014,0.00001689188,0.00004236543,0.000007910327],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005112569,"about_ca_system_score_gemma":0.00001816418,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006320611,"about_ca_topic_score_gemma":0.00000202618,"domain_scores_codex":[0.999285,0.0000149488,0.000229007,0.000127355,0.0001975746,0.0001461434],"domain_scores_gemma":[0.9997106,0.000051643,0.00003500364,0.000106799,0.00002038593,0.00007557995],"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":[0.00003145552,0.0001382003,0.2958784,0.00006266232,0.00006152401,0.00002187991,0.0000612322,0.00004505576,0.683244,0.00001044466,0.0000345877,0.02041058],"study_design_scores_gemma":[0.005592097,0.000681876,0.6355295,0.003259818,0.0001955021,0.00004656734,0.0003330691,0.01745345,0.3355573,0.0001297225,0.0008053074,0.0004158678],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9802089,0.00007483675,0.01918556,0.0003113711,0.00009166654,0.00006711911,0.000002146198,0.00003316992,0.00002520452],"genre_scores_gemma":[0.99844,0.0000332074,0.001156757,0.000004597984,0.0001253358,0.000008291125,0.000004726263,0.00001018575,0.000216908],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3476868,"threshold_uncertainty_score":0.1938384,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0197742161831527,"score_gpt":0.3226499961593369,"score_spread":0.3028757799761841,"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."}}