{"id":"W2588159480","doi":"10.29173/cais543","title":"Refining Ranked Retrieval Results for Legal Discovery Search Through Supervised Rank Aggregation","year":2013,"lang":"fr","type":"article","venue":"Proceedings of the Annual Conference of CAIS / Actes du congrès annuel de l ACSI","topic":"Data Mining Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Ranking (information retrieval); Information retrieval; Relevance (law); Computer science; Context (archaeology); Metasearch engine; Rank (graph theory); Artificial intelligence; Humanities; Data mining; Mathematics; Geography; Philosophy; Political science; Search engine; Combinatorics; Web search query","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow","scholarly_communication"],"consensus_categories":["scholarly_communication"],"category_scores_codex":[0.001519886,0.0004377373,0.0006984338,0.0001481427,0.0004309078,0.01074395,0.003609465,0.0003071122,0.00001604874],"category_scores_gemma":[0.01649537,0.0003734432,0.0003253845,0.001076964,0.0008666145,0.04342048,0.001227933,0.0005033163,0.00001445971],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001080793,"about_ca_system_score_gemma":0.0007288068,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002479083,"about_ca_topic_score_gemma":0.0000185362,"domain_scores_codex":[0.9962873,0.00006203026,0.001136066,0.0008475632,0.0008246477,0.0008423511],"domain_scores_gemma":[0.9456019,0.0006812925,0.001090689,0.0006350021,0.05179172,0.0001993662],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001618118,0.001178839,0.006241334,0.002478333,0.0006783681,0.000003322096,0.1276805,0.00007388213,0.06133407,0.5058214,0.06157952,0.2313123],"study_design_scores_gemma":[0.01106175,0.002852327,0.05341689,0.005317342,0.000625144,0.0001636623,0.008629637,0.1880957,0.24125,0.02861445,0.4575318,0.002441228],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9330752,0.0008924732,0.01235088,0.04042853,0.0008985394,0.001584336,0.002509087,0.0001045504,0.008156365],"genre_scores_gemma":[0.9508647,0.0002709768,0.03886039,0.0003158136,0.0003782782,0.0001233095,0.00006429022,0.00004340116,0.009078883],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4772069,"threshold_uncertainty_score":0.9998717,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05107547805967532,"score_gpt":0.2819600116558389,"score_spread":0.2308845335961636,"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."}}