{"id":"W3049132276","doi":"10.1038/s41598-020-69076-x","title":"Synthetic photoplethysmogram generation using two Gaussian functions","year":2020,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Non-Invasive Vital Sign Monitoring","field":"Engineering","cited_by":57,"is_retracted":false,"has_abstract":true,"ca_institutions":"B.C. Women's Hospital & Health Centre; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Photoplethysmogram; Beat (acoustics); Gaussian; Standard deviation; Pattern recognition (psychology); Computer science; Algorithm; Mathematics; Artificial intelligence; Acoustics; Statistics; Physics; Computer vision","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":[0.0002816979,0.0001485901,0.0001344656,0.0001054245,0.0002893529,0.0003392821,0.00008007579,0.00004399436,0.000120924],"category_scores_gemma":[0.00008726905,0.0001560389,0.00008073499,0.0006495709,0.00007512943,0.000257572,0.00003790435,0.0001291754,0.00007620415],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000928572,"about_ca_system_score_gemma":0.00004405022,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001953915,"about_ca_topic_score_gemma":0.0000100426,"domain_scores_codex":[0.9985475,0.00002074383,0.000331495,0.0004869667,0.0003273872,0.0002859388],"domain_scores_gemma":[0.999232,0.00001214411,0.00006600594,0.0004331316,0.00006412241,0.0001926276],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[4.947513e-7,0.000007781762,0.001426228,0.00001939368,0.00001235288,0.0001392777,0.0001883919,0.03415524,0.9607317,0.00000831411,0.002107296,0.001203526],"study_design_scores_gemma":[0.0001155552,0.00001954692,0.00008719794,0.00004106019,0.00004736796,0.0002189804,0.0001247517,0.06812956,0.9122638,0.0003718544,0.01818037,0.0003999903],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9254806,0.0001872845,0.05429132,0.00005798292,0.01691888,0.0002854045,0.000003183685,0.000556506,0.002218863],"genre_scores_gemma":[0.9967682,0.000001079463,0.00236056,0.00001602678,0.0006432175,0.00001764983,0.000025892,0.00003803132,0.0001292966],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07128767,"threshold_uncertainty_score":0.636308,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03664574455488682,"score_gpt":0.2463307393483104,"score_spread":0.2096849947934236,"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."}}