{"id":"W2618277072","doi":"10.1186/2193-7680-3-2","title":"RETRACTED ARTICLE: Investigative advising: a job for Bayes","year":2014,"lang":"en","type":"article","venue":"Crime Science","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":2,"is_retracted":true,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Bayes' theorem; Context (archaeology); Prior probability; Computer science; Bayesian probability; Suspect; Bayes factor; Machine learning; Artificial intelligence; Relevance (law); Psychology; Criminology; Political science","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":["research_integrity"],"consensus_categories":[],"category_scores_codex":[0.009035727,0.0007964232,0.001038433,0.001799089,0.002766443,0.00539339,0.002539248,0.01032419,0.03942371],"category_scores_gemma":[0.1011034,0.0004844628,0.001042141,0.001336642,0.004382108,0.006476992,0.002103838,0.01326543,0.02275526],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002908495,"about_ca_system_score_gemma":0.002970438,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004814055,"about_ca_topic_score_gemma":0.004611754,"domain_scores_codex":[0.9933668,0.003094584,0.0007315666,0.0006605813,0.001903874,0.000242622],"domain_scores_gemma":[0.937184,0.03621221,0.001472259,0.003184142,0.02019089,0.00175644],"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":[0.00002717974,0.00001249848,0.0001460755,0.00009827701,0.000009883894,0.000303992,0.00015614,0.0001406657,0.00004235032,0.0199597,0.950938,0.02816519],"study_design_scores_gemma":[0.00001861796,0.00001525632,0.0002566133,0.000585528,0.0000188631,0.0006196702,0.0002573358,0.001022448,0.0001749626,0.06148293,0.9355084,0.00003940618],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.0003356402,0.004795151,0.008667099,0.7451054,0.2279555,0.00005376125,0.0002554485,0.0002899388,0.0125422],"genre_scores_gemma":[0.03551491,0.01986343,0.02152674,0.362388,0.4275969,0.0002209715,0.0004573612,0.0008043651,0.1316272],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9896758,"threshold_uncertainty_score":0.1318854,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04239514789203764,"score_gpt":0.2946854622354405,"score_spread":0.2522903143434029,"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."}}