{"id":"W4200627709","doi":"10.1016/j.bspc.2021.103434","title":"Automated newborn cry diagnostic system using machine learning approach","year":2021,"lang":"en","type":"article","venue":"Biomedical Signal Processing and Control","topic":"Infant Health and Development","field":"Health Professions","cited_by":34,"is_retracted":false,"has_abstract":false,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Bill and Melinda Gates Foundation","keywords":"Mel-frequency cepstrum; Infant crying; Feature (linguistics); Artificial intelligence; Computer science; Set (abstract data type); Prosody; Feature extraction; Support vector machine; Pattern recognition (psychology); Speech recognition; Feature vector; Machine learning; Crying; Psychology","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":["sts"],"consensus_categories":[],"category_scores_codex":[0.001054306,0.0002224715,0.0004832381,0.0001029278,0.001807394,0.00004670571,0.00009695985,0.0003323012,0.0001020057],"category_scores_gemma":[0.0005152578,0.0001753622,0.00004396912,0.0003867738,0.0001169076,0.00008990739,0.00007963696,0.0008072841,0.00003493724],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001748114,"about_ca_system_score_gemma":0.001764479,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001887232,"about_ca_topic_score_gemma":0.000003954602,"domain_scores_codex":[0.9968904,0.0006768746,0.0007368625,0.000433024,0.0004644896,0.0007983452],"domain_scores_gemma":[0.9980231,0.0007991377,0.0002594025,0.0001019511,0.0002484543,0.000567947],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001473813,0.001321436,0.1861414,0.0402238,0.0005358791,0.001808386,0.01227891,0.0004946,0.02614411,0.00153274,0.004336202,0.7237087],"study_design_scores_gemma":[0.004035637,0.00008030688,0.004043987,0.001672052,0.00009665002,0.0001118645,0.002210512,0.9560993,0.00002504072,0.00003213924,0.031266,0.0003265365],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2010594,0.03071724,0.7485909,0.00336197,0.001339138,0.002185064,0.0001030335,0.003739175,0.008904051],"genre_scores_gemma":[0.9909784,0.00003663305,0.006383265,0.001625554,0.000521382,0.00008654019,0.00009023591,0.00003052008,0.0002474347],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9556047,"threshold_uncertainty_score":0.9994921,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02638727020039237,"score_gpt":0.3346106481958181,"score_spread":0.3082233779954258,"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."}}