{"id":"W7066767233","doi":"","title":"Indicium ex Machina: Unstructured Sentencing and Disparate Outcomes in Canada","year":2023,"lang":"en","type":"article","venue":"eYLS (Yale Law School)","topic":"Particle Detector Development and Performance","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Normative; Criminal justice; Extant taxon; Jurisdiction; Process (computing); Sentencing guidelines; Incentive","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":[0.004847074,0.0003153915,0.0006948846,0.002511939,0.03099447,0.005461098,0.003397237,0.002418597,0.004623402],"category_scores_gemma":[0.027177,0.0004743279,0.0004672351,0.004477176,0.006441166,0.001480657,0.005333164,0.00595083,0.0002857551],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.1435234,"about_ca_system_score_gemma":0.2833116,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9974436,"about_ca_topic_score_gemma":0.9988272,"domain_scores_codex":[0.9899536,0.001007427,0.0002945118,0.0008538853,0.003074006,0.004816504],"domain_scores_gemma":[0.9838592,0.002933949,0.001428056,0.0006877574,0.006056945,0.005034114],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0008220419,0.0006505394,0.522316,0.0001905742,0.0002707635,0.004857614,0.08625968,0.004583328,0.001048047,0.1780399,0.07669829,0.1242633],"study_design_scores_gemma":[0.0002568869,0.0003277638,0.7229582,0.0005680262,0.0003206992,0.001310585,0.1676987,0.01382752,0.001334604,0.02643114,0.06434967,0.0006162139],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9436142,0.0005774425,0.0007391803,0.01728011,0.0001132673,0.0001713138,0.000834626,0.00004364045,0.03662615],"genre_scores_gemma":[0.9921192,0.0003420982,0.000408434,0.001681154,0.00001348251,0.00003792545,0.0002350258,0.00001986358,0.005142782],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1435234,"threshold_uncertainty_score":0.9933915,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009479665814869252,"score_gpt":0.2259310306219591,"score_spread":0.2164513648070898,"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."}}