{"id":"W2964026269","doi":"10.1145/3322640.3326742","title":"Statute Law Information Retrieval and Entailment","year":2019,"lang":"en","type":"article","venue":"","topic":"Topic Modeling","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Paragraph; Textual entailment; Logical consequence; Statute; Information retrieval; Natural language processing; Artificial intelligence; Law; Political science; World Wide Web","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009004904,0.00002982075,0.00003319804,0.00001793571,0.00001861199,0.0001020748,0.0001140829,0.00001398936,0.00002445341],"category_scores_gemma":[0.000002121954,0.00002490142,0.000006563512,0.00003845315,0.000004587037,0.001069837,0.0001139414,0.0000284524,0.0001438859],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001282918,"about_ca_system_score_gemma":0.00001039415,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003825107,"about_ca_topic_score_gemma":0.000002110975,"domain_scores_codex":[0.9996625,0.000005285189,0.00007993788,0.00006909382,0.0001101005,0.00007309236],"domain_scores_gemma":[0.9997597,0.000009164519,0.00001768223,0.0001707305,0.00001634411,0.00002639169],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000002508584,0.000002431387,0.000370075,0.000006718129,0.00000187834,1.843795e-7,0.0004638509,0.00007577646,0.0001278379,0.9870737,0.0001088783,0.01176611],"study_design_scores_gemma":[0.0009299857,0.0001189994,0.003447828,0.00001405048,0.000002279857,0.00001117557,0.0001415511,0.8399257,0.006037413,0.01133469,0.1377916,0.0002447009],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1962242,0.00001119874,0.7531227,0.0006635456,0.0002514342,0.0001126322,3.063401e-7,0.00008021831,0.04953381],"genre_scores_gemma":[0.9585195,0.000003359879,0.03971648,0.001218873,0.000007703805,4.48125e-7,8.282702e-7,7.464778e-7,0.0005320486],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9757391,"threshold_uncertainty_score":0.184941,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008123675063360826,"score_gpt":0.2120289732397921,"score_spread":0.2039052981764313,"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."}}