{"id":"W4414292738","doi":"10.1007/s11282-025-00862-x","title":"U-net-based segmentation of foreign bodies and ghost images in panoramic radiographs","year":2025,"lang":"en","type":"article","venue":"Oral Radiology","topic":"Dental Radiography and Imaging","field":"Dentistry","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University","funders":"","keywords":"Foreign Bodies; Segmentation; Radiography; Image segmentation; Market segmentation","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":[0.0001985465,0.0001428604,0.0003263898,0.0006709,0.00005144625,0.00002401316,0.0001266896,0.00009152594,0.00004193739],"category_scores_gemma":[0.00004685528,0.0001405321,0.0001005848,0.000352601,0.0003791394,0.000143019,0.00003240783,0.000129104,0.000004360491],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002774193,"about_ca_system_score_gemma":0.00002394609,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002653658,"about_ca_topic_score_gemma":0.0001626737,"domain_scores_codex":[0.9989614,0.0001428802,0.0003164299,0.0002653739,0.00007442653,0.0002395001],"domain_scores_gemma":[0.9995313,0.0001485407,0.00009429182,0.0001670612,0.00002769619,0.00003112566],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001277139,0.00005770478,0.9581932,0.00009395327,0.00005575265,0.0000498214,0.00007552304,0.00004433045,0.02734162,0.001794443,0.002101208,0.01006477],"study_design_scores_gemma":[0.002435534,0.0001405272,0.9599739,0.00008959027,0.00006967849,0.00007301489,0.0006659041,0.0008597695,0.02862588,0.006436912,0.0004221937,0.0002071427],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9897743,0.003056237,0.00188414,0.0001037791,0.0003180568,0.0002139208,0.00003671889,0.00003854288,0.004574279],"genre_scores_gemma":[0.9977695,0.0001479785,0.001605677,0.0002197338,0.00001272271,0.00002083416,0.0000484329,0.000008986304,0.0001661695],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009857625,"threshold_uncertainty_score":0.5730732,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008762735991595594,"score_gpt":0.2755232236563841,"score_spread":0.2667604876647885,"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."}}