{"id":"W2031923940","doi":"10.3389/fnins.2014.00410","title":"Brain fingerprinting classification concealed information test detects US Navy military medical information with P300","year":2014,"lang":"en","type":"article","venue":"Frontiers in Neuroscience","topic":"Deception detection and forensic psychology","field":"Psychology","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"U.S. Navy; Uniformed Services University of the Health Sciences; York University","keywords":"Navy; Word error rate; Statistical analysis; Computer science; Medicine; Statistics; Psychology; Artificial intelligence; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.001391223,0.0004833328,0.0003574696,0.001394132,0.0001387923,0.0003561978,0.0003093006,0.0007077486,0.001621248],"category_scores_gemma":[0.01266922,0.0001050104,0.0002963117,0.0004238446,0.0003242719,0.0005694667,0.0004057014,0.0004074927,0.0002709735],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001760804,"about_ca_system_score_gemma":0.0001206944,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000646833,"about_ca_topic_score_gemma":0.0004527552,"domain_scores_codex":[0.9986492,0.0002451982,0.0001623576,0.0002117823,0.0005959996,0.0001355452],"domain_scores_gemma":[0.9936108,0.003265646,0.001534941,0.0004778717,0.0008083465,0.0003023046],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.009218093,0.000971958,0.6896229,0.0002296013,0.0002453694,0.001648499,0.0005149231,0.001248021,0.2006748,0.0004830719,0.0008781395,0.09426442],"study_design_scores_gemma":[0.00006423186,0.00292231,0.9304064,0.00001788393,0.00006299972,0.003111615,0.0001096696,0.008187482,0.05437664,0.0002792256,0.0004284381,0.00003303309],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.994383,0.00009197485,0.004587315,0.00002484106,0.00001750416,0.0000701275,0.0001297704,0.000042263,0.0006532105],"genre_scores_gemma":[0.9974599,0.00002721113,0.002099065,0.00002592656,0.000008542989,0.00002756057,0.0001285156,0.000006718668,0.0002164706],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001621248,"threshold_uncertainty_score":0.007357538,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01077747305243019,"score_gpt":0.2656126607086547,"score_spread":0.2548351876562245,"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."}}