{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.05518956,0.001588671,0.002524276,0.004597624,0.002711351,0.01093618,0.003652922,0.005979063,0.0142423],"category_scores_gemma":[0.190427,0.001297035,0.001857764,0.003298244,0.02033484,0.02081661,0.004415719,0.01253296,0.005385339],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003867568,"about_ca_system_score_gemma":0.005161442,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003820573,"about_ca_topic_score_gemma":0.002037553,"domain_scores_codex":[0.9644023,0.02395419,0.001957587,0.003164314,0.006047602,0.0004739503],"domain_scores_gemma":[0.8306826,0.1449125,0.003739662,0.009925398,0.009243922,0.001495858],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00008811134,0.00004006801,0.0007778227,0.000452161,0.00009680368,0.0001318313,0.001140688,0.003639291,0.0001379334,0.8303988,0.04224053,0.1208559],"study_design_scores_gemma":[0.00001458073,0.000004911552,0.00005488952,0.000159333,0.00001004205,0.00003902001,0.00008292679,0.003354553,0.0000590112,0.9818469,0.01435838,0.00001547929],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002014467,0.008805056,0.8055727,0.1559982,0.002933813,0.0001629448,0.0003050917,0.0008030395,0.02340467],"genre_scores_gemma":[0.136564,0.009539067,0.8186992,0.01788316,0.009154766,0.0005019798,0.0002840311,0.0005814219,0.00679234],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.05518956,"threshold_uncertainty_score":0.2918738,"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."}}