{"id":"W4406778160","doi":"10.53106/256299802024120601001","title":"Legal Entity Identifier and Future Research Directions","year":2024,"lang":"en","type":"article","venue":"International Journal of Computer Auditing","topic":"Artificial Intelligence in Law","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Identifier; Computer science; Data science; Political science; Programming language","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"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":[0.05725763,0.001058708,0.002737545,0.0100846,0.004104232,0.01572489,0.006443338,0.01109354,0.05436284],"category_scores_gemma":[0.05921126,0.0008889202,0.001892675,0.01213973,0.01176211,0.04520491,0.004015231,0.006490735,0.009043133],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006144486,"about_ca_system_score_gemma":0.02074075,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01084245,"about_ca_topic_score_gemma":0.008601988,"domain_scores_codex":[0.9838035,0.00840566,0.001266328,0.002373368,0.002872874,0.001278283],"domain_scores_gemma":[0.8564827,0.08626186,0.006777164,0.006582495,0.03657584,0.007319954],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0008238967,0.001528038,0.01144148,0.004443021,0.0001484186,0.0003921645,0.001450342,0.001232049,0.0007814209,0.4097195,0.08009671,0.4879431],"study_design_scores_gemma":[0.0003311835,0.0009035969,0.006043115,0.01222184,0.0004776168,0.001586043,0.01631865,0.00896121,0.001898292,0.4129282,0.5381218,0.0002084136],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"review","genre_gemma":"empirical","genre_scores_codex":[0.01545701,0.4176621,0.02868806,0.4152629,0.009350249,0.0004440099,0.001569724,0.0007275713,0.1108384],"genre_scores_gemma":[0.2203719,0.5751478,0.08999836,0.05244176,0.01551415,0.0009308471,0.00391473,0.000226684,0.04145377],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.05725763,"threshold_uncertainty_score":0.3028108,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0682778041795773,"score_gpt":0.4531467859311281,"score_spread":0.3848689817515508,"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."}}