{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.000878373,0.00007298576,0.0000845469,0.00009293247,0.0008708848,0.00005691684,0.0001250802,0.0001064394,0.00195697],"category_scores_gemma":[0.0004431159,0.00007363752,0.00005823506,0.0009465874,0.0001421417,0.0003821223,0.00003408537,0.00009360369,0.001192232],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001475538,"about_ca_system_score_gemma":0.0001175264,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01515848,"about_ca_topic_score_gemma":0.00327877,"domain_scores_codex":[0.9987657,0.0001537158,0.0001896886,0.0001944451,0.0003589303,0.0003375034],"domain_scores_gemma":[0.9993988,0.0001993181,0.00004182087,0.0001415718,0.00009748025,0.0001210305],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"qualitative","study_design_scores_codex":[0.0006127926,0.0008408914,0.04515585,0.0000888775,0.0002646368,0.01521481,0.1576772,0.007172068,0.1709407,0.4805357,0.0149591,0.1065374],"study_design_scores_gemma":[0.0007317149,0.0002845386,0.003162827,0.0001793011,0.0002512762,0.000907052,0.3888688,0.1093914,0.1323198,0.09326069,0.2679455,0.002697064],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9674562,0.000007015581,0.001006964,0.0006180018,0.0008400456,0.0001938528,0.000002497208,0.000374926,0.02950055],"genre_scores_gemma":[0.9923241,0.00002867567,0.0004869954,0.00006606668,0.0003569201,0.000001038331,0.000002271915,0.00001160896,0.006722387],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3872751,"threshold_uncertainty_score":0.9995854,"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."}}