{"id":"W2137754152","doi":"10.1017/s1472669606000831","title":"Legal Information Retrieval Study – Lexis Professional and Westlaw UK","year":2006,"lang":"en","type":"article","venue":"Legal Information Management","topic":"Legal Education and Practice Innovations","field":"Social Sciences","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Institute for Catastrophic Loss Reduction","keywords":"Lexis; Legal research; Computer science; Political science; Information retrieval; Library science; Law; Linguistics; Philosophy","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"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":[0.01051078,0.0002237545,0.0005844789,0.006175977,0.003366601,0.005701534,0.000528217,0.00146154,0.02558349],"category_scores_gemma":[0.07727022,0.0005464441,0.0002574337,0.008022476,0.002560831,0.00843238,0.002287394,0.001485075,0.003414414],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.009631469,"about_ca_system_score_gemma":0.007062217,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05357008,"about_ca_topic_score_gemma":0.0776715,"domain_scores_codex":[0.9795046,0.006745403,0.002118807,0.001142181,0.009285572,0.001203399],"domain_scores_gemma":[0.8765757,0.0779237,0.0132874,0.004143893,0.02354515,0.00452416],"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":[0.001649189,0.002257172,0.128134,0.003081791,0.0001267026,0.00148312,0.1261064,0.0002137515,0.003175946,0.04019802,0.1729762,0.5205979],"study_design_scores_gemma":[0.0003099185,0.002026343,0.4508772,0.003403284,0.0001590217,0.001830687,0.07658501,0.0005509614,0.00346659,0.003230652,0.4574382,0.0001222682],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6771743,0.03377198,0.0009673443,0.04537625,0.0006115312,0.0007390571,0.002709815,0.0001047089,0.2385451],"genre_scores_gemma":[0.8942803,0.0117147,0.001084358,0.004580575,0.0003757837,0.0005358984,0.001207061,0.00009536809,0.0861259],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05357008,"threshold_uncertainty_score":0.1065165,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01552587512751165,"score_gpt":0.3348233183128624,"score_spread":0.3192974431853507,"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."}}