{"id":"W3173215294","doi":"10.3390/app11135850","title":"Computer-Assisted Detection of Cemento-Enamel Junction in Intraoral Ultrasonographs","year":2021,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Dental Radiography and Imaging","field":"Dentistry","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Innovates; China Scholarship Council; Mitacs; Children's Hospital Foundation; Stollery Children’s Hospital Foundation; Alberta Innovates - Technology Futures; Women and Children's Health Research Institute; Children's Health Research Institute","keywords":"Enamel paint; Cementum; Artificial intelligence; Preprocessor; Computer vision; Computer science; Materials science; Dentistry; Medicine; Dentin","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002972655,0.00008570818,0.0001351595,0.0002633579,0.0001267059,0.00009179806,0.0001469808,0.00003310994,0.00005195948],"category_scores_gemma":[0.00000730337,0.00008402144,0.00007139379,0.00191606,0.0002573594,0.0002072558,0.00003426172,0.00009946607,0.0000148021],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002032905,"about_ca_system_score_gemma":0.00002578081,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009674608,"about_ca_topic_score_gemma":0.0003825364,"domain_scores_codex":[0.9989396,0.00003625332,0.0002437741,0.0002927438,0.0002910929,0.0001964813],"domain_scores_gemma":[0.999676,0.00004594487,0.00009529178,0.000116802,0.00003015261,0.00003579409],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00003434349,0.0002971146,0.1394416,0.00004211435,0.00002811198,0.00002560154,0.0004570427,0.0002226807,0.7150894,0.001917404,0.0001526075,0.142292],"study_design_scores_gemma":[0.0007556084,0.00008647366,0.7184515,0.00004437131,0.00001956892,0.00008077773,0.003081796,0.002077548,0.2738903,0.001013256,0.0002921862,0.0002066566],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9858772,0.000110477,0.006860512,0.00001770623,0.0007571848,0.00008112341,0.000001939396,0.00003535871,0.006258531],"genre_scores_gemma":[0.9983342,0.00001013895,0.001518773,0.00005734131,0.00003992962,0.000009552874,0.000005421245,0.000003768767,0.00002091844],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.57901,"threshold_uncertainty_score":0.3426295,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01754647458584678,"score_gpt":0.2562346759918355,"score_spread":0.2386882014059887,"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."}}