{"id":"W2148972377","doi":"10.1145/1571941.1572114","title":"Reciprocal rank fusion outperforms condorcet and individual rank learning methods","year":2009,"lang":"en","type":"article","venue":"","topic":"Text and Document Classification Technologies","field":"Computer Science","cited_by":568,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Reciprocal; Condorcet method; Rank (graph theory); Computer science; Simple (philosophy); Mean reciprocal rank; Fusion; Fuse (electrical); Artificial intelligence; Learning to rank; Machine learning; Mathematics; Ranking (information retrieval); Combinatorics; Engineering; Voting","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":[0.01554707,0.002794731,0.002737776,0.005251543,0.002288471,0.003396012,0.002762933,0.002555219,0.006495238],"category_scores_gemma":[0.02099973,0.0004563766,0.002099348,0.004145848,0.001244684,0.00568732,0.002756121,0.003364278,0.005738601],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00199599,"about_ca_system_score_gemma":0.002802499,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008063242,"about_ca_topic_score_gemma":0.01253845,"domain_scores_codex":[0.9846497,0.00431429,0.000765527,0.002311881,0.007043764,0.0009148035],"domain_scores_gemma":[0.9879893,0.004027053,0.0006910707,0.003832174,0.003033789,0.0004265897],"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":[0.00131588,0.0006315913,0.005160831,0.0005821103,0.0008779478,0.0000958363,0.0002095626,0.04700416,0.009096009,0.00690101,0.05460455,0.8735205],"study_design_scores_gemma":[0.0003429592,0.001844854,0.006793503,0.0001706639,0.0007020358,0.0008692479,0.0003470441,0.8564595,0.05348266,0.03150867,0.04719779,0.0002810071],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1458109,0.01680315,0.7205831,0.002949137,0.001442351,0.0008806008,0.00472032,0.05194733,0.05486314],"genre_scores_gemma":[0.5817881,0.002358673,0.3791952,0.0006820528,0.0007266329,0.0002913376,0.009000432,0.002226807,0.02373068],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01554707,"threshold_uncertainty_score":0.08222175,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04029236246797672,"score_gpt":0.3261430280054581,"score_spread":0.2858506655374814,"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."}}