{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001729247,0.0005194742,0.000542629,0.002768891,0.0003982457,0.000993625,0.001015494,0.0009377724,0.002308015],"category_scores_gemma":[0.00412281,0.0005275644,0.0004925879,0.001273247,0.0003353482,0.001287763,0.0009468555,0.0007068228,0.001694239],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004036333,"about_ca_system_score_gemma":0.001188873,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002941148,"about_ca_topic_score_gemma":0.003790379,"domain_scores_codex":[0.9988064,0.0002125583,0.0001153166,0.000249776,0.0005734023,0.00004250541],"domain_scores_gemma":[0.9969477,0.0006389503,0.0002401588,0.0002562294,0.001840408,0.00007653855],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001828517,0.0001557249,0.04069597,0.0002482935,0.00009654334,0.0002003542,0.0002006223,0.004285207,0.1792802,0.003610739,0.0024901,0.7685534],"study_design_scores_gemma":[0.00009628235,0.001456535,0.1781508,0.0002900388,0.0004326558,0.007698917,0.0005798918,0.4337949,0.3225179,0.005145525,0.04946641,0.0003701195],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0388383,0.0007360285,0.955903,0.0001724392,0.0001299293,0.0002403101,0.0004488619,0.001138376,0.002392781],"genre_scores_gemma":[0.1301009,0.0006974597,0.8647087,0.00006873666,0.00007329243,0.0002730647,0.0004484665,0.00009821482,0.0035312],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002941148,"threshold_uncertainty_score":0.00914526,"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."}}