{"id":"W4385573352","doi":"10.18653/v1/2022.nllp-1.24","title":"Detecting Relevant Differences Between Similar Legal Texts","year":2022,"lang":"en","type":"article","venue":"","topic":"Topic Modeling","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Metadata; Computer science; Task (project management); Terminology; Natural language processing; Sentence; Artificial intelligence; Information retrieval; Focus (optics); Variation (astronomy); Resource (disambiguation); Legal case; Data science; World Wide Web; Linguistics; Political science; Law","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001719204,0.0005972636,0.0005720232,0.004404023,0.001036841,0.001390188,0.0009462937,0.001532544,0.002422629],"category_scores_gemma":[0.009460577,0.000299429,0.0006935327,0.001566478,0.000789928,0.003302698,0.001486409,0.001345516,0.001236811],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001128102,"about_ca_system_score_gemma":0.0009640543,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003396433,"about_ca_topic_score_gemma":0.006258101,"domain_scores_codex":[0.9978319,0.0004511985,0.0002142138,0.0008731922,0.0004358296,0.0001936582],"domain_scores_gemma":[0.992911,0.003802097,0.001153453,0.0005581061,0.00119393,0.0003813708],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002138606,0.000905922,0.1176109,0.001961465,0.0003366427,0.003059733,0.005815369,0.01059613,0.1691217,0.02123176,0.0506252,0.6165966],"study_design_scores_gemma":[0.000291425,0.0007109395,0.2229946,0.0005257394,0.0005139409,0.003833635,0.007309406,0.4869249,0.1179196,0.06312127,0.09558546,0.0002692234],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7772233,0.00295928,0.1919083,0.002176721,0.0005456826,0.0004541017,0.008246349,0.004448977,0.01203727],"genre_scores_gemma":[0.8700157,0.0003816759,0.1113946,0.0003689751,0.000312753,0.000215736,0.01464391,0.0003104168,0.002356177],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004404023,"threshold_uncertainty_score":0.009092152,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03539416592959268,"score_gpt":0.2492053513097113,"score_spread":0.2138111853801186,"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."}}