{"id":"W1522891069","doi":"10.1109/iembs.2006.259591","title":"Risk Factors for Apgar Score using Artificial Neural Networks","year":2006,"lang":"en","type":"article","venue":"","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Children's Hospital of Eastern Ontario; Carleton University; University of Ottawa","funders":"","keywords":"Artificial neural network; Computer science; Apgar score; Artificial intelligence; Machine learning; Pregnancy; Biology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005098921,0.0005274538,0.000368177,0.0009760914,0.0001451483,0.0007011122,0.0003763728,0.0003805872,0.001061045],"category_scores_gemma":[0.003924842,0.0001503654,0.0003286632,0.0007609229,0.0001273161,0.0004854283,0.0002685522,0.0005172394,0.0002584388],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003119962,"about_ca_system_score_gemma":0.0004063011,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009015626,"about_ca_topic_score_gemma":0.005735319,"domain_scores_codex":[0.9997755,0.00007535991,0.00002132619,0.0000415016,0.00006889275,0.00001751204],"domain_scores_gemma":[0.999255,0.000470638,0.0001086882,0.00003128031,0.0001158371,0.00001865486],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003742597,0.0001799928,0.07995575,0.0001407944,0.0002929718,0.0004264496,0.00009649398,0.5577247,0.003058692,0.004660595,0.002948609,0.3501407],"study_design_scores_gemma":[0.00001104957,0.00004055094,0.009231733,0.00003345049,0.00003265975,0.00008622836,0.00001915454,0.9851987,0.0006057624,0.003968631,0.0007557209,0.00001633437],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.212726,0.002673865,0.7733364,0.001383897,0.0002061648,0.0001377943,0.0008401921,0.001998626,0.006697191],"genre_scores_gemma":[0.9337555,0.001272091,0.06206176,0.00006951466,0.0001223726,0.00008970712,0.0006625943,0.00003181809,0.001934714],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009015626,"threshold_uncertainty_score":0.01792628,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01767471320209705,"score_gpt":0.2188822042824136,"score_spread":0.2012074910803166,"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."}}