{"id":"W6945359222","doi":"10.25384/sage.c.5216460.v1","title":"Correctional Intake Assessment and Case Planning: Application Development and Validation","year":2020,"lang":"en","type":"other","venue":"Sage Journals Data","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Process (computing); Plan (archaeology); Criminal history; Dynamic assessment; Risk assessment; Substance use","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":[0.04811285,0.001005438,0.0006023124,0.004574366,0.001145874,0.001680099,0.002526391,0.0006113761,0.01446743],"category_scores_gemma":[0.07943273,0.0008552562,0.0008043277,0.002784661,0.0007718354,0.001250709,0.003050354,0.001042691,0.00449636],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003033984,"about_ca_system_score_gemma":0.01290979,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03190244,"about_ca_topic_score_gemma":0.04346344,"domain_scores_codex":[0.9829007,0.009626333,0.002023978,0.001065435,0.003742061,0.0006415126],"domain_scores_gemma":[0.9358453,0.03155474,0.001493702,0.006627429,0.02270385,0.001775047],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001514915,0.004304808,0.05116581,0.0008897752,0.0001006203,0.0003663525,0.004908295,0.004526769,0.002788883,0.001332933,0.01882246,0.9092784],"study_design_scores_gemma":[0.006684104,0.008664693,0.4812388,0.005165646,0.000665462,0.002134187,0.01548409,0.1730313,0.03060622,0.006948274,0.2688419,0.0005353515],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4274237,0.000784007,0.2975866,0.001513365,0.0003305372,0.1850273,0.01311937,0.01482982,0.05938523],"genre_scores_gemma":[0.2878001,0.0009841019,0.6152181,0.0002461605,0.00009026413,0.07642571,0.0079002,0.0007995421,0.01053573],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04811285,"threshold_uncertainty_score":0.2544481,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1092642835067137,"score_gpt":0.3860404245197943,"score_spread":0.2767761410130805,"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."}}