{"id":"W4233310726","doi":"10.32920/14638728","title":"Investigative advising: a job for Bayes","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Data Analysis with R","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Bayes' theorem; Context (archaeology); Prior probability; Bayesian probability; Computer science; Suspect; Machine learning; Bayes factor; Relevance (law); Artificial intelligence; Econometrics; Mathematics; Psychology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000358774,0.0002516638,0.0004296196,0.0001668277,0.00007794346,0.001011131,0.002175273,0.0001650692,0.00005321471],"category_scores_gemma":[0.0003646649,0.0002213538,0.0002631362,0.0003387681,0.00008377765,0.0004182211,0.003941004,0.0002899485,0.00002722736],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000609599,"about_ca_system_score_gemma":0.000548814,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001483644,"about_ca_topic_score_gemma":0.0001835702,"domain_scores_codex":[0.9979526,0.00009068377,0.0003267539,0.001048246,0.0003125794,0.0002691105],"domain_scores_gemma":[0.9974394,0.0001765814,0.0001898779,0.001723899,0.0003216724,0.0001485168],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001176721,0.0003514457,0.002498589,0.00122791,0.002612807,0.0001512703,0.01060016,0.003795781,0.00316966,0.6178002,0.2436914,0.1140891],"study_design_scores_gemma":[0.0006563563,0.0001117466,0.003356494,0.0006539457,0.000277544,0.00001573817,0.000454643,0.7068115,0.02593887,0.2281827,0.03160599,0.00193454],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002451039,0.0001890444,0.9877714,0.003576023,0.0005045667,0.0002989357,0.00002741251,0.0002282373,0.004953351],"genre_scores_gemma":[0.06469167,0.00001769621,0.9299935,0.001709142,0.0001449475,0.0001451741,0.0002078603,0.00001907446,0.003070908],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.7030157,"threshold_uncertainty_score":0.9750357,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04785452740659544,"score_gpt":0.2949278632178987,"score_spread":0.2470733358113032,"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."}}