{"id":"W4385572004","doi":"10.18653/v1/2023.acl-long.777","title":"U-CREAT: Unsupervised Case Retrieval using Events extrAcTion","year":2023,"lang":"en","type":"article","venue":"","topic":"Artificial Intelligence in Law","field":"Social Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Margin (machine learning); Relevance (law); Ranking (information retrieval); Benchmark (surveying); Task (project management); Information retrieval; Artificial intelligence; Baseline (sea); Pipeline (software); Natural language processing; Machine learning","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":[0.001867048,0.001622946,0.0008833829,0.01217023,0.001343279,0.002448565,0.002892883,0.001389172,0.01054058],"category_scores_gemma":[0.009328299,0.0005288122,0.001283522,0.005248818,0.0008793959,0.004119629,0.002781175,0.001142684,0.006008674],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001239988,"about_ca_system_score_gemma":0.00285059,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02278471,"about_ca_topic_score_gemma":0.03205923,"domain_scores_codex":[0.9976895,0.0004417589,0.0002897609,0.0007301391,0.0006503636,0.0001985306],"domain_scores_gemma":[0.9966221,0.001210902,0.0003391642,0.0009599714,0.0007210835,0.0001468298],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003667714,0.0005260514,0.008745971,0.001516556,0.0002361554,0.001209828,0.0009504292,0.01019243,0.03493814,0.01407105,0.1444261,0.7828206],"study_design_scores_gemma":[0.0004540616,0.0004346326,0.03672726,0.0004195254,0.0004097644,0.005258276,0.002556819,0.3937817,0.1325702,0.03473809,0.3922855,0.0003642379],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1039012,0.003376718,0.6988221,0.00126955,0.0005033282,0.004391577,0.05251658,0.08394518,0.0512737],"genre_scores_gemma":[0.1802529,0.001065767,0.6760342,0.0004029501,0.0002141918,0.001024192,0.1256139,0.001474667,0.01391726],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02278471,"threshold_uncertainty_score":0.04530418,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1966583509986925,"score_gpt":0.4658018421290703,"score_spread":0.2691434911303778,"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."}}