{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009585756,0.001186339,0.002074294,0.016822,0.001571414,0.005936464,0.002287898,0.001366459,0.003334523],"category_scores_gemma":[0.0342104,0.000618648,0.001350735,0.00680946,0.0005859639,0.004035282,0.001536601,0.001069558,0.002948988],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001677602,"about_ca_system_score_gemma":0.003608601,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01080853,"about_ca_topic_score_gemma":0.0193604,"domain_scores_codex":[0.9925324,0.001834756,0.0008808982,0.0007591347,0.003600907,0.0003918349],"domain_scores_gemma":[0.9748358,0.01119138,0.002763098,0.003313151,0.00739012,0.0005064211],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008290598,0.0007348292,0.01803631,0.0008505328,0.0004054093,0.000322424,0.001146754,0.02045764,0.02270449,0.007641909,0.01410908,0.9127616],"study_design_scores_gemma":[0.0003455197,0.0009447759,0.01231712,0.0002800253,0.0007199605,0.0008834834,0.000890288,0.8600726,0.08216326,0.01920818,0.02186556,0.0003092503],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1639588,0.003586167,0.7911915,0.001236561,0.000181376,0.0009338204,0.003380495,0.02704099,0.008490317],"genre_scores_gemma":[0.3305209,0.0005017969,0.6618811,0.0001291342,0.0001219959,0.000281288,0.003329412,0.0004146334,0.002819796],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.016822,"threshold_uncertainty_score":0.05069494,"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."}}