{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001035634,0.00009944178,0.0002177487,0.00005818122,0.0000673477,0.00006488911,0.000253572,0.00003470446,0.00007805718],"category_scores_gemma":[0.00004850057,0.00009485603,0.00009910727,0.0004301097,0.00003675517,0.0001356875,0.00008091908,0.00007519709,0.0000726406],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001907733,"about_ca_system_score_gemma":0.00004471912,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006898425,"about_ca_topic_score_gemma":0.000005244858,"domain_scores_codex":[0.9991188,0.00007329296,0.000210026,0.0002220701,0.0002020507,0.000173785],"domain_scores_gemma":[0.9995572,0.00004554121,0.0001027538,0.0001559533,0.00005098136,0.00008757134],"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":[0.00003213526,0.0002523226,0.01669764,0.0008952842,0.0007360055,0.0004398791,0.01607872,0.3856053,0.3681409,0.0005595582,0.001381686,0.2091805],"study_design_scores_gemma":[0.0001679354,0.00002059504,0.0004824592,0.00003763073,0.00002696486,0.00001382404,0.0001469127,0.9907793,0.008052481,0.00002999564,0.0001143192,0.0001275259],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9631701,0.00003135599,0.03528916,0.0007247104,0.00007506967,0.00005540649,0.00001003468,0.0001233167,0.0005208111],"genre_scores_gemma":[0.9676154,0.000007793657,0.03214875,0.0001431245,0.00002337227,2.355491e-7,0.000001649177,0.000006820184,0.00005282499],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.605174,"threshold_uncertainty_score":0.3868117,"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."}}