{"id":"W4389205280","doi":"10.17986/blm.1661","title":"Yapay Zeka ve Adli Bilimler: Yayınların Bibliyometrik Analizi","year":2023,"lang":"en","type":"article","venue":"The Bulletin of Legal Medicine","topic":"Explainable Artificial Intelligence (XAI)","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Forensic science; Data science; Computer science; History; Archaeology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.003613152,0.0005466708,0.0009530268,0.04458175,0.001040157,0.004178312,0.0006314306,0.0005935268,0.01910024],"category_scores_gemma":[0.01180146,0.0002280631,0.0008266297,0.04490593,0.0006232196,0.002388058,0.0009563636,0.000477788,0.003953242],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002085168,"about_ca_system_score_gemma":0.004169926,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006028917,"about_ca_topic_score_gemma":0.005767816,"domain_scores_codex":[0.9965723,0.0003512275,0.0007116615,0.0003375796,0.001836396,0.000190924],"domain_scores_gemma":[0.9906821,0.003705908,0.001620277,0.0002821991,0.003541304,0.0001681527],"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":[0.0006478688,0.0001676748,0.2355142,0.02169643,0.0007447727,0.002418994,0.005140537,0.001289029,0.004216519,0.0101244,0.04102619,0.6770133],"study_design_scores_gemma":[0.00008784253,0.000297753,0.644583,0.009239192,0.001800576,0.006475425,0.01708749,0.004336904,0.007605571,0.006040577,0.3022484,0.0001972303],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6143978,0.1314648,0.01524836,0.007009743,0.0008763967,0.00141633,0.06586953,0.001412516,0.1623045],"genre_scores_gemma":[0.8208091,0.09052078,0.02969064,0.000422193,0.0006373053,0.001277579,0.03678908,0.0001712334,0.01968202],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9554182,"threshold_uncertainty_score":0.06389672,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0381693571336104,"score_gpt":0.2923963437566728,"score_spread":0.2542269866230624,"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."}}