{"id":"W3028352403","doi":"10.1016/j.compbiomed.2020.103794","title":"Automated integration of facial and intra-oral images of anterior teeth","year":2020,"lang":"en","type":"article","venue":"Computers in Biology and Medicine","topic":"Dental Radiography and Imaging","field":"Dentistry","cited_by":15,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Artificial intelligence; Computer vision; Segmentation; Anterior teeth; Process (computing); Pattern recognition (psychology); Orthodontics; Medicine","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":[0.00009786335,0.00006331679,0.0002444804,0.00007558081,0.0000132093,0.000001853779,0.00005151864,0.00004736291,0.000009587012],"category_scores_gemma":[0.00005302419,0.0000478067,0.0000154023,0.000125591,0.0004834803,0.00004016327,0.00003585689,0.00007672489,2.666797e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000002364025,"about_ca_system_score_gemma":0.000004028836,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005304611,"about_ca_topic_score_gemma":0.000006945389,"domain_scores_codex":[0.9995255,0.00004849647,0.0002103486,0.0001193339,0.00002868636,0.0000676401],"domain_scores_gemma":[0.9997888,0.00005149903,0.00006993047,0.00003954827,0.00001615232,0.00003402966],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002518055,0.00003967236,0.4264389,0.0001736673,0.00005896038,0.00003987814,0.00291763,8.737275e-7,0.3973239,0.0007550012,0.001921744,0.1700779],"study_design_scores_gemma":[0.002443065,0.0007568301,0.9748951,0.0003681706,0.00003098098,0.00007284464,0.0006464411,0.008224351,0.01167863,0.0005896948,0.0001936504,0.0001003015],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9937861,0.001204146,0.00397469,0.0004538885,0.0003393436,0.00006319082,0.000009870235,0.00002740323,0.0001413778],"genre_scores_gemma":[0.9982311,0.000177372,0.001245955,0.0002824317,0.000044873,6.828777e-7,0.00001367474,0.000002176112,0.000001714922],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5484561,"threshold_uncertainty_score":0.1949501,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01557519634169471,"score_gpt":0.3179213777610957,"score_spread":0.3023461814194011,"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."}}