{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0003269971,0.0003483435,0.0005206371,0.00005599034,0.00188186,0.0001015186,0.0006764531,0.0002525703,0.00320736],"category_scores_gemma":[0.0006284605,0.0003250255,0.000268486,0.0007778124,0.0006177401,0.0002298726,0.0003857138,0.0003860277,0.001923421],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002436656,"about_ca_system_score_gemma":0.0002118004,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003053352,"about_ca_topic_score_gemma":0.0009494094,"domain_scores_codex":[0.9966277,0.0002176141,0.000480377,0.0006622417,0.000870218,0.001141843],"domain_scores_gemma":[0.9981946,0.0002284573,0.0001260068,0.0003134148,0.0001449102,0.0009926477],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002148306,0.0005424678,0.01304221,0.0001188378,0.0002207255,0.0001395115,0.04697327,0.00002073832,0.001011449,0.1437119,0.7384134,0.05559071],"study_design_scores_gemma":[0.0004176382,0.0002677654,0.00262889,0.00001502967,0.00005086056,0.000001110333,0.001070465,0.0000538877,0.0003489492,0.004401745,0.9902873,0.0004563567],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.2072977,0.006670447,0.0004083471,0.07604351,0.002007774,0.001063433,0.000152186,0.001269507,0.7050871],"genre_scores_gemma":[0.948237,0.0007979323,0.001354338,0.00875164,0.003973736,0.0000694744,0.00002767043,0.00007196604,0.03671623],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7409394,"threshold_uncertainty_score":0.9999202,"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."}}