{"id":"W4414713696","doi":"10.33899/jes.v34i4.49670","title":"Predicting Arrest Release Outcomes: A Comparative Analysis of Machine Learning Models","year":2025,"lang":"en","type":"article","venue":"Mağallaẗ al-tarbiyaẗ wa-al-ʻilm","topic":"Emergency and Acute Care Studies","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Random forest; Categorical variable; Logistic regression; Discriminative model; Predictive modelling; Criminal justice; Binomial regression; Classifier (UML); Predictive analytics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02390492,0.001422908,0.001047744,0.004857256,0.0004720258,0.002076064,0.000989585,0.00117242,0.0009914757],"category_scores_gemma":[0.03440653,0.0002843636,0.001864719,0.002032165,0.0005087642,0.002122186,0.0009484211,0.001183638,0.0003654757],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001545048,"about_ca_system_score_gemma":0.001150932,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01415836,"about_ca_topic_score_gemma":0.01244885,"domain_scores_codex":[0.9904597,0.006738957,0.0006276353,0.0006988353,0.001151657,0.0003232534],"domain_scores_gemma":[0.9583467,0.0373612,0.001084587,0.001024951,0.001876572,0.0003059166],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00296531,0.0009790523,0.488786,0.0007060578,0.002859702,0.0002819867,0.0005349943,0.2745115,0.0006407343,0.00382409,0.006962602,0.216948],"study_design_scores_gemma":[0.000112523,0.001879625,0.08884723,0.0002968097,0.0005713027,0.0001415713,0.0006863829,0.9013455,0.0008840972,0.002690411,0.002467521,0.0000770195],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9294844,0.01429655,0.04053641,0.004407476,0.0003183397,0.0002487967,0.002024061,0.0007156772,0.007968302],"genre_scores_gemma":[0.9860473,0.001759599,0.009802949,0.000186583,0.0001246797,0.00006559362,0.001424425,0.00005526762,0.0005335514],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02390492,"threshold_uncertainty_score":0.1264228,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03876022604130576,"score_gpt":0.3342853554971104,"score_spread":0.2955251294558047,"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."}}