{"id":"W4394168234","doi":"10.6084/m9.figshare.7507103","title":"Potential contribution of periapical radiographic film image processing for forensic identification","year":2018,"lang":"en","type":"dataset","venue":"Figshare","topic":"Dental Radiography and Imaging","field":"Dentistry","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Identification (biology); Forensic identification; Forensic science; Dentistry; Radiography; Forensic dentistry; Orthodontics; Computer science; Artificial intelligence; Pattern recognition (psychology); Medicine; Biology; Radiology; Genetics; Veterinary medicine; Botany","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.007108386,0.0007237003,0.0006438921,0.00457034,0.00057437,0.002032693,0.001936418,0.0008842798,0.01043863],"category_scores_gemma":[0.02668265,0.0002277276,0.0006137927,0.003446967,0.0003948108,0.0007572558,0.001355282,0.0006172577,0.004164901],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006519082,"about_ca_system_score_gemma":0.001092075,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00354346,"about_ca_topic_score_gemma":0.005573735,"domain_scores_codex":[0.9949699,0.001469104,0.0009863483,0.0008632053,0.001391146,0.0003202283],"domain_scores_gemma":[0.9841009,0.007883577,0.001447178,0.003432041,0.002981525,0.0001548029],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.002246194,0.0003597126,0.266154,0.0101973,0.0006302145,0.001419625,0.0009099331,0.004672642,0.01000334,0.004724792,0.2544263,0.4442559],"study_design_scores_gemma":[0.0001868013,0.0002641924,0.2606978,0.002076497,0.0007967541,0.002541825,0.0008458871,0.0119276,0.0174656,0.004954437,0.6981003,0.0001424643],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.2121236,0.008873834,0.03002211,0.002820812,0.001214068,0.002022015,0.7081015,0.004165545,0.03065657],"genre_scores_gemma":[0.3665995,0.001883475,0.06795178,0.0005630899,0.0002761999,0.001847787,0.5528846,0.0006933198,0.00730036],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.01043863,"threshold_uncertainty_score":0.03759319,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01507168393906512,"score_gpt":0.2839308696919812,"score_spread":0.2688591857529161,"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."}}