{"id":"W4213191780","doi":"10.1007/s12626-022-00105-z","title":"Overview and Discussion of the Competition on Legal Information Extraction/Entailment (COLIEE) 2021","year":2022,"lang":"en","type":"article","venue":"The Review of Socionetwork Strategies","topic":"Topic Modeling","field":"Computer Science","cited_by":80,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Hokkaido University; Shizuoka University; University of Alberta; National Institute of Informatics; Alberta Machine Intelligence Institute","keywords":"Task (project management); Statute; Computer science; Logical consequence; Component (thermodynamics); Competition (biology); Natural language processing; Information retrieval; Law; Artificial intelligence; Political science","routes":{"ca_aff":true,"ca_fund":true,"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":[0.06747521,0.003246669,0.003443195,0.01230799,0.005836769,0.01158124,0.006276617,0.004500407,0.03971196],"category_scores_gemma":[0.06808843,0.001107528,0.002772821,0.01242097,0.002067359,0.01002832,0.009134989,0.00476075,0.02631189],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.008516013,"about_ca_system_score_gemma":0.01169891,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02881249,"about_ca_topic_score_gemma":0.05269529,"domain_scores_codex":[0.9531818,0.01919245,0.002834266,0.004494868,0.01675992,0.003536739],"domain_scores_gemma":[0.9140526,0.02834608,0.001557854,0.007124057,0.0395042,0.009415219],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003812971,0.0004255842,0.0008966734,0.001217063,0.00009700645,0.00008134999,0.0004456859,0.001738109,0.002032159,0.005445294,0.8278228,0.1594171],"study_design_scores_gemma":[0.000179295,0.0003106623,0.00396632,0.0005185223,0.00005700268,0.0001955731,0.0006071583,0.009491152,0.00429147,0.006774875,0.973464,0.0001439378],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06291305,0.09021676,0.2832319,0.1208966,0.05654792,0.00946552,0.09895205,0.04033627,0.23744],"genre_scores_gemma":[0.1027212,0.01731185,0.2938264,0.02121551,0.00951148,0.00655691,0.35854,0.02195465,0.1683621],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.06747521,"threshold_uncertainty_score":0.3568473,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02041053936864444,"score_gpt":0.2813343749363553,"score_spread":0.2609238355677108,"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."}}