{"id":"W2924262975","doi":"10.5539/jmbr.v9n1p33","title":"Determining Gender and Age by Mandibular Anatomy Landmarks in Computed Tomography with Cone-Beam (CBCT)","year":2019,"lang":"en","type":"article","venue":"Journal of Molecular Biology Research","topic":"Dental Radiography and Imaging","field":"Dentistry","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Mental foramen; Cone beam computed tomography; Mandibular canal; Inferior alveolar nerve; Computed tomography; Medicine; Orthodontics; Mandible (arthropod mouthpart); Alveolar crest; Significant difference; Dentistry; Mathematics; Anatomy; Radiography; Molar; Dental alveolus; Radiology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001227836,0.000134933,0.0003456242,0.0009222855,0.0000605161,0.00008268859,0.0002929768,0.0001378875,0.00004469149],"category_scores_gemma":[0.00003227569,0.0001054175,0.00009839096,0.000612405,0.0002895406,0.0001208406,0.0001119855,0.0008357845,0.000008341748],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002565533,"about_ca_system_score_gemma":0.00004146115,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002333322,"about_ca_topic_score_gemma":0.000007900879,"domain_scores_codex":[0.9982174,0.0004195215,0.0003296663,0.0002497432,0.0003510464,0.0004326154],"domain_scores_gemma":[0.9992405,0.0001463527,0.0001309329,0.000188091,0.0001526909,0.0001413932],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002264748,0.00007315919,0.777711,0.00003792436,0.0001920635,0.003631243,0.00008893521,0.000006770922,0.2166193,0.0001116439,0.0004705837,0.0008308013],"study_design_scores_gemma":[0.01264072,0.002844697,0.9006107,0.0004962565,0.0000785224,0.008225177,0.001384681,0.0004963267,0.06485647,0.002104605,0.005555324,0.0007064547],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9939905,0.003688269,0.0009435408,0.00004348029,0.0001105632,0.0001688512,0.000006843753,0.000006327444,0.001041657],"genre_scores_gemma":[0.9990989,0.000076581,0.000637113,0.0001018168,0.00002508689,0.000002583846,0.0000114544,0.00001752418,0.00002895302],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1517629,"threshold_uncertainty_score":0.42988,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01889361680232972,"score_gpt":0.3415584841265598,"score_spread":0.3226648673242301,"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."}}