{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003008733,0.0009531829,0.001372102,0.005796846,0.001086542,0.002414569,0.002043542,0.001728418,0.007410035],"category_scores_gemma":[0.01998961,0.000431418,0.001467489,0.002563468,0.0008305787,0.004779738,0.001739122,0.001130693,0.003412213],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001487679,"about_ca_system_score_gemma":0.001701096,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007377424,"about_ca_topic_score_gemma":0.00760012,"domain_scores_codex":[0.9941967,0.001443091,0.0007326618,0.001047555,0.0022333,0.0003466564],"domain_scores_gemma":[0.992782,0.003627015,0.0005428742,0.00101344,0.00186506,0.0001695579],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0005895378,0.0008639302,0.009653918,0.001030981,0.0001496876,0.0005660839,0.001017643,0.01940721,0.0676745,0.01963715,0.02594027,0.8534691],"study_design_scores_gemma":[0.0001718986,0.0004562558,0.0160329,0.0001267443,0.0001687086,0.001184955,0.0005765155,0.7508386,0.1529839,0.04198286,0.03532587,0.0001508342],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1103483,0.001079726,0.8545172,0.00151984,0.0001221845,0.001170067,0.00314577,0.01428856,0.01380845],"genre_scores_gemma":[0.4041036,0.0004075821,0.577317,0.0005806218,0.0001735439,0.0005059356,0.01131324,0.0004023388,0.005196113],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007410035,"threshold_uncertainty_score":0.02478909,"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."}}