{"id":"W3039698533","doi":"10.38146/bsz.2020.1.1","title":"Genetika és bűnüldözés – Az igazságügyi célú DNS-vizsgálatok első negyedszázada Magyarországon II.","year":2020,"lang":"en","type":"article","venue":"Belügyi Szemle","topic":"Korean Peninsula Historical and Political Studies","field":"Social Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Forensic science; Forensic genetics; Law enforcement; Quarter (Canadian coin); Criminology; Law; DNA profiling; Genealogy; Political science; Genetics; History; DNA; Biology; Sociology; Archaeology; Gene; Microsatellite","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":[0.001003707,0.0006144752,0.0005061445,0.001123069,0.001127115,0.002662161,0.0004354564,0.0007436983,0.01433105],"category_scores_gemma":[0.001109211,0.0002136805,0.0002953989,0.001253623,0.001337937,0.001155462,0.001382848,0.0009139726,0.003851335],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001799517,"about_ca_system_score_gemma":0.002289612,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007028488,"about_ca_topic_score_gemma":0.006741588,"domain_scores_codex":[0.9995189,0.00008647226,0.00003726468,0.0001494061,0.00009907638,0.0001088435],"domain_scores_gemma":[0.999624,0.00007548234,0.00008621372,0.00004853191,0.0001097892,0.00005600338],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001032664,0.0002307673,0.1594766,0.001051937,0.00024749,0.009046341,0.008841457,0.002393693,0.02445595,0.1556984,0.03772013,0.5998046],"study_design_scores_gemma":[0.00006279032,0.0001823646,0.2013263,0.000796112,0.0001644754,0.01039274,0.0119116,0.001321345,0.01445802,0.02395859,0.7353083,0.0001173233],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7050039,0.02995751,0.02922944,0.01349759,0.00184522,0.0002175507,0.008345677,0.0007037721,0.2111994],"genre_scores_gemma":[0.8977532,0.01404234,0.01402,0.001684186,0.0001534208,0.0001084707,0.004245612,0.0002029261,0.0677898],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01433105,"threshold_uncertainty_score":0.0479421,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05713463861671109,"score_gpt":0.2933105670461189,"score_spread":0.2361759284294078,"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."}}