{"id":"W4406523726","doi":"10.3138/utlj-2024-0002","title":"LexOptima: The promise of AI-enabled legal systems","year":2025,"lang":"en","type":"article","venue":"University of Toronto Law Journal","topic":"Artificial Intelligence in Law","field":"Social Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Political science; Business; Law and economics; Law; Sociology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.009266756,0.0005567804,0.0005604615,0.00169733,0.003596814,0.01160727,0.002859248,0.003203379,0.01150086],"category_scores_gemma":[0.02057847,0.000508126,0.0007823723,0.00172758,0.01058297,0.01566527,0.009689195,0.004661236,0.002562317],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00574676,"about_ca_system_score_gemma":0.01014352,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01130782,"about_ca_topic_score_gemma":0.01088593,"domain_scores_codex":[0.9942211,0.002812871,0.0002602573,0.0006654167,0.00167005,0.0003703644],"domain_scores_gemma":[0.9869501,0.005727129,0.0007799048,0.004038331,0.001208209,0.00129633],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00004057734,0.0000436567,0.0006140032,0.0001180462,0.00002030246,0.0000878536,0.0006679338,0.01418283,0.0005776361,0.9064162,0.01752693,0.05970404],"study_design_scores_gemma":[0.00002088785,0.00002684144,0.0002618666,0.0001156929,0.000009328128,0.00006314494,0.0003656186,0.04247872,0.0006207944,0.8031491,0.1528586,0.00002931168],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02098099,0.004083816,0.6411715,0.1454847,0.001011391,0.0003931282,0.0009176384,0.005117922,0.180839],"genre_scores_gemma":[0.4713066,0.005054185,0.4868315,0.006732605,0.0008100264,0.0005003571,0.001161586,0.00105885,0.0265443],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01160727,"threshold_uncertainty_score":0.04900783,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01563691265120517,"score_gpt":0.283403782770766,"score_spread":0.2677668701195609,"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."}}