{"id":"W4285130490","doi":"10.1109/access.2022.3181167","title":"NICUface: Robust Neonatal Face Detection in Complex NICU Scenes","year":2022,"lang":"en","type":"article","venue":"IEEE Access","topic":"Neonatal and fetal brain pathology","field":"Medicine","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"Children's Hospital of Eastern Ontario; University of Ottawa; Carleton University","funders":"Science and Engineering Research Council","keywords":"Robustness (evolution); Computer science; Neonatal intensive care unit; Artificial intelligence; Face detection; Face (sociological concept); Facial recognition system; Detector; Computer vision; Pattern recognition (psychology); Medicine; Pediatrics; Telecommunications","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":[0.0001735081,0.0001306658,0.0002480288,0.0001737507,0.0001557828,0.00002016823,0.0002652177,0.00005563739,0.0006562515],"category_scores_gemma":[0.00003018662,0.0001264927,0.00006555693,0.0004387612,0.00006823411,0.0001741093,0.0002410642,0.0003876409,0.00004377561],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001163384,"about_ca_system_score_gemma":0.00003910833,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002282738,"about_ca_topic_score_gemma":0.0002223929,"domain_scores_codex":[0.9988495,0.00007774759,0.0002244864,0.0003255945,0.000252909,0.0002697229],"domain_scores_gemma":[0.9995493,0.00004931457,0.00006418525,0.0002332643,0.00002824771,0.00007565117],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.004056503,0.001096588,0.0461648,0.0004799625,0.0001349156,0.004566647,0.002658702,0.01880192,0.310637,0.000195988,0.007713335,0.6034936],"study_design_scores_gemma":[0.01282247,0.001885261,0.6371894,0.00008173382,0.0001329159,0.00681932,0.003846456,0.02078882,0.1920363,0.001533823,0.1214792,0.001384333],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9944066,0.0003530947,0.001735311,0.000937651,0.000661836,0.0003894685,0.00003446844,0.0000918785,0.001389667],"genre_scores_gemma":[0.9975365,0.00001517521,0.0001048515,0.001161193,0.0001190855,0.0000856686,0.00005389977,0.00002042059,0.0009032181],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6021093,"threshold_uncertainty_score":0.7185494,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05607680368567912,"score_gpt":0.3145280870958558,"score_spread":0.2584512834101766,"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."}}