{"id":"W2567134213","doi":"10.1016/j.forsciint.2016.12.013","title":"Development of a biometric method to estimate age on hand radiographs","year":2016,"lang":"en","type":"article","venue":"Forensic Science International","topic":"Forensic Anthropology and Bioarchaeology Studies","field":"Arts and Humanities","cited_by":14,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"AGE-WELL","keywords":"Biometrics; Forensic anthropology; Context (archaeology); Multivariate statistics; Linear discriminant analysis; Radiography; Statistics; Mathematics; Multivariate analysis; Age groups; Sample (material); Orthodontics; Medicine; Demography; Computer science; Artificial intelligence; Geography; Surgery","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0004818721,0.00009571396,0.0001346488,0.001002403,0.0004021837,0.00002628753,0.0003420733,0.00002086814,0.000517286],"category_scores_gemma":[0.0001751769,0.00005403476,0.00004506516,0.0002709923,0.01622397,0.0001110807,0.0001570899,0.0000341191,0.00007956495],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005023362,"about_ca_system_score_gemma":0.00006285562,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001012553,"about_ca_topic_score_gemma":0.001699689,"domain_scores_codex":[0.9989488,0.00001185107,0.0002092172,0.000252415,0.0003582646,0.0002194961],"domain_scores_gemma":[0.9993946,0.0001280186,0.00007527129,0.0001013973,0.0002470251,0.00005367192],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00007483499,0.00005220095,0.001521011,0.00000252371,0.00007080416,0.000007776447,0.01142592,0.000001407726,0.001959445,0.7903638,0.001891357,0.1926289],"study_design_scores_gemma":[0.002167615,0.001737571,0.03881967,0.0004231967,0.00004860143,0.00004675192,0.01117366,0.0002447377,0.3803031,0.04316815,0.5209193,0.0009476144],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9253114,0.00004286039,0.01859978,0.01556095,0.00517139,0.0002476901,0.00006611682,0.00006632306,0.03493351],"genre_scores_gemma":[0.9433091,0.00000204904,0.05458279,0.0002350494,0.0001035449,0.00001133472,0.000002329064,0.000003889866,0.001749963],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7471957,"threshold_uncertainty_score":0.9864533,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05022882809117527,"score_gpt":0.3581114375498035,"score_spread":0.3078826094586282,"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."}}