{"id":"W2961834871","doi":"10.1097/prs.0000000000005673","title":"Facial Recognition Technology: A Primer for Plastic Surgeons","year":2019,"lang":"en","type":"article","venue":"Plastic & Reconstructive Surgery","topic":"Face recognition and analysis","field":"Computer Science","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hospital for Sick Children; University of Toronto","funders":"","keywords":"Medicine; Facial recognition system; Plastic surgery; Feature (linguistics); Relevance (law); Face (sociological concept); Artificial intelligence; Surgery; Pattern recognition (psychology); Computer science","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003241115,0.001259126,0.0006987526,0.002253602,0.001149694,0.00292702,0.001617766,0.004982012,0.008188069],"category_scores_gemma":[0.006114115,0.0005101893,0.0006326899,0.001220861,0.003196412,0.007622209,0.001865566,0.008733508,0.00843508],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008787911,"about_ca_system_score_gemma":0.00200942,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008453735,"about_ca_topic_score_gemma":0.001563896,"domain_scores_codex":[0.9988564,0.0003354441,0.0001264117,0.00009208461,0.0005197388,0.00006979181],"domain_scores_gemma":[0.9971301,0.001489661,0.0001603956,0.0001303348,0.0007267823,0.0003628345],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00004218253,0.0001830778,0.0008421845,0.0007121664,0.00001308283,0.0007251386,0.0007849809,0.0005111618,0.002287209,0.02903222,0.3493194,0.6155472],"study_design_scores_gemma":[0.000005146368,0.00005632515,0.0006695957,0.001343958,0.000006875793,0.00585652,0.0005785023,0.0004051244,0.0002929565,0.02091518,0.9698399,0.0000298048],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.002256135,0.5002142,0.08793092,0.2930611,0.03239607,0.0001856645,0.0001908549,0.0008582229,0.08290681],"genre_scores_gemma":[0.01904908,0.6404963,0.1109895,0.1046145,0.05065389,0.0008123371,0.00028015,0.0004020006,0.07270235],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.008188069,"threshold_uncertainty_score":0.02739179,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02002180486095873,"score_gpt":0.2277356106660843,"score_spread":0.2077138058051256,"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."}}