{"id":"W4291632994","doi":"10.2196/39917","title":"Training and Profiling a Pediatric Facial Expression Classifier for Children on Mobile Devices: Machine Learning Study","year":2022,"lang":"en","type":"article","venue":"JMIR Formative Research","topic":"Emotion and Mood Recognition","field":"Psychology","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of Biomedical Imaging and Bioengineering; National Institute of General Medical Sciences; Islamic Development Bank; U.S. National Library of Medicine; Weston Havens Foundation; Bill and Melinda Gates Foundation; Eunice Kennedy Shriver National Institute of Child Health and Human Development; Hartwell Foundation; Wu Tsai Neurosciences Institute, Stanford University; National Institutes of Health; National Science Foundation","keywords":"Computer science; Sadness; Convolutional neural network; Artificial intelligence; Facial expression; Facial expression recognition; Disgust; Machine learning; Classifier (UML); Anger; Facial recognition system; Pattern recognition (psychology); Psychology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008645736,0.0008252886,0.0004212481,0.000508048,0.0002105054,0.000379181,0.000570456,0.0005312212,0.001287213],"category_scores_gemma":[0.003355443,0.000178636,0.0004562391,0.0004562261,0.0002219676,0.0006025913,0.0003282956,0.0007409073,0.0006532129],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008825967,"about_ca_system_score_gemma":0.0004362001,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009769408,"about_ca_topic_score_gemma":0.009607102,"domain_scores_codex":[0.999371,0.0001512307,0.0000351726,0.0001909993,0.0001421389,0.0001094797],"domain_scores_gemma":[0.9985694,0.0008988683,0.00008255621,0.000125859,0.0002627417,0.00006052771],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0007650771,0.0009078188,0.132035,0.0002726202,0.0002326793,0.0006821324,0.0005429671,0.144984,0.01761198,0.001394079,0.01225413,0.6883174],"study_design_scores_gemma":[0.00002869343,0.0007548233,0.04992988,0.0000576751,0.00007468183,0.0004879966,0.000519792,0.9287954,0.01576911,0.0005723443,0.002978883,0.00003073439],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9608475,0.0009298222,0.03394284,0.0003635745,0.00007604023,0.0001332851,0.0005861215,0.0005037193,0.002617035],"genre_scores_gemma":[0.9653412,0.0005367567,0.0304155,0.0001177833,0.00003592868,0.0001124048,0.001072781,0.00004876436,0.002319101],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009769408,"threshold_uncertainty_score":0.01942509,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1398498578831423,"score_gpt":0.4528776521375235,"score_spread":0.3130277942543812,"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."}}