{"id":"W2042813317","doi":"10.1080/13811110701250176","title":"The Development and Validation of Statistical Prediction Rules for Discriminating Between Genuine and Simulated Suicide Notes","year":2007,"lang":"en","type":"article","venue":"Archives of Suicide Research","topic":"Suicide and Self-Harm Studies","field":"Psychology","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Discriminant function analysis; Receiver operating characteristic; Linear discriminant analysis; Sentence; Sample (material); Computer science; Statistics; Affect (linguistics); Poison control; Artificial intelligence; Psychology; Data mining; Machine learning; Natural language processing; Econometrics; Mathematics; Medicine; Medical emergency","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002419864,0.0001108413,0.0002356262,0.0002347393,0.0003943088,0.0000204788,0.0001290968,0.00004954328,0.000007948765],"category_scores_gemma":[0.0013138,0.00008113464,0.0000281831,0.0001214855,0.0006706167,0.00004905469,0.000154276,0.0001831008,0.00000130842],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000122904,"about_ca_system_score_gemma":0.00003448686,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003836034,"about_ca_topic_score_gemma":0.0001550662,"domain_scores_codex":[0.9982327,0.0002035584,0.000536826,0.0002561826,0.000328015,0.0004426736],"domain_scores_gemma":[0.9798208,0.01968495,0.0001057934,0.0001575853,0.0001502102,0.00008064767],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0007314276,0.00007222901,0.6884399,0.0001904377,0.0003002496,0.000003058628,0.01509098,0.000008018551,0.007569089,0.01000088,0.00002752695,0.2775662],"study_design_scores_gemma":[0.0006931679,0.0003255795,0.9500319,0.00003725237,0.0000337317,0.000001588217,0.004834217,0.000183549,0.03215124,0.01154263,0.00008531019,0.00007980639],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9805222,0.0009026317,0.01692974,0.0001873642,0.00002988846,0.0004957991,0.00009110969,0.00001359987,0.0008277207],"genre_scores_gemma":[0.9925552,0.00009761095,0.00710118,0.000003436426,0.00005821452,0.00002124971,0.00007551182,0.00001592918,0.00007169219],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2774864,"threshold_uncertainty_score":0.3308575,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1442873098111558,"score_gpt":0.4407447480569701,"score_spread":0.2964574382458143,"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."}}