{"id":"W3033434602","doi":"10.3390/s20113171","title":"Fully Automatic Landmarking of Syndromic 3D Facial Surface Scans Using 2D Images","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Face recognition and analysis","field":"Computer Science","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"Alberta Children's Hospital; University of Calgary","funders":"National Institute of Dental and Craniofacial Research; National Institutes of Health","keywords":"Landmark; Computer science; Artificial intelligence; Computer vision; Biometrics; Ground truth; Pattern recognition (psychology); Face (sociological concept); Set (abstract data type)","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":[0.0009359117,0.0007472823,0.0008533425,0.001297191,0.000210501,0.0008519999,0.0007392464,0.0005956573,0.001535914],"category_scores_gemma":[0.003198362,0.0004928877,0.0008370883,0.0006424705,0.0004227059,0.0006080522,0.0009180335,0.0005988051,0.001207101],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002522711,"about_ca_system_score_gemma":0.000641771,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002143692,"about_ca_topic_score_gemma":0.004278241,"domain_scores_codex":[0.9992244,0.0001887462,0.00003975951,0.0001871294,0.0003065699,0.00005331524],"domain_scores_gemma":[0.9989327,0.0003982208,0.00009616411,0.0002868959,0.0002615002,0.0000244686],"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":[0.0005514908,0.00009290739,0.01149562,0.0002293816,0.0001481082,0.0004104261,0.0005962037,0.04620942,0.2317901,0.001189665,0.002765865,0.7045209],"study_design_scores_gemma":[0.00006684455,0.000418953,0.03967603,0.00004732437,0.0001131075,0.002186808,0.0005319408,0.8085463,0.1397444,0.002775087,0.005778254,0.0001150138],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1721592,0.0003046895,0.8212888,0.0001179572,0.00004416818,0.0001902096,0.0004998221,0.004407035,0.0009881331],"genre_scores_gemma":[0.4614453,0.0003379851,0.5343741,0.00007146474,0.00002479923,0.0002390889,0.00149489,0.0005864326,0.001425841],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002143692,"threshold_uncertainty_score":0.005138159,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02756159978834074,"score_gpt":0.246709909209841,"score_spread":0.2191483094215002,"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."}}