{"id":"W4388769056","doi":"10.1017/s1472669623000476","title":"Yemisi Dina","year":2023,"lang":"en","type":"article","venue":"Legal Information Management","topic":"Artificial Intelligence in Law","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Rest (music); Political science; Association (psychology); Library science; Law; Sociology; Psychology; Computer science; Medicine","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":[0.003665705,0.001096817,0.0009846813,0.001355112,0.008444182,0.01088762,0.001286528,0.004517845,0.08216564],"category_scores_gemma":[0.01262466,0.000481112,0.0005845114,0.00135435,0.002596538,0.004753327,0.00456425,0.01304021,0.04577805],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01000465,"about_ca_system_score_gemma":0.01767823,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05804165,"about_ca_topic_score_gemma":0.07823535,"domain_scores_codex":[0.9953945,0.0007542509,0.0001874956,0.0009636336,0.001746453,0.0009537053],"domain_scores_gemma":[0.9907604,0.0008762064,0.0003520391,0.0003972082,0.004741766,0.002872315],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002799242,0.000007309365,0.0001612431,0.00004590793,0.000003806193,0.00006698485,0.0001570202,0.00001308626,0.00007718848,0.005739792,0.9845086,0.009190957],"study_design_scores_gemma":[0.000001964754,0.000002733405,0.0001666433,0.00005373224,0.000001713661,0.0000435954,0.0002330459,0.0000115419,0.00003443484,0.0003435336,0.999101,0.000005999941],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.001138493,0.04432188,0.0008061869,0.5795766,0.1231469,0.00005584145,0.001068033,0.0003678362,0.2495182],"genre_scores_gemma":[0.01173956,0.01553403,0.0008003252,0.1827938,0.007218069,0.00008939733,0.0006341478,0.0002907721,0.7808999],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.08216564,"threshold_uncertainty_score":0.2748714,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03388920262072554,"score_gpt":0.3450654901272633,"score_spread":0.3111762875065378,"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."}}