{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007501042,0.0001244288,0.0001601067,0.0001474123,0.0002060639,0.00026078,0.0005565053,0.000111847,0.00003670608],"category_scores_gemma":[0.0001629367,0.00009088519,0.00003352474,0.0002948893,0.00006488518,0.0005816484,0.0002359593,0.0002409107,0.00003097756],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001452276,"about_ca_system_score_gemma":0.00002014796,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003976441,"about_ca_topic_score_gemma":5.527925e-7,"domain_scores_codex":[0.9989361,0.00008060106,0.0002097054,0.0003460079,0.0002042915,0.0002232618],"domain_scores_gemma":[0.9993972,0.0001181975,0.00007602625,0.0003118026,0.00003660022,0.00006024426],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000005739527,0.00001702684,0.0007480193,0.000002022571,0.000004699802,0.000001004627,0.0003668522,0.000004846911,0.00296929,0.07665597,0.0006408013,0.9185838],"study_design_scores_gemma":[0.00714894,0.002926986,0.1681686,0.00008221654,0.00006198471,0.0001422785,0.003148523,0.1444578,0.2467341,0.1920366,0.2329379,0.002154037],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04832601,0.0002566224,0.9379131,0.005465303,0.0001164271,0.0001640124,2.252233e-7,0.001773828,0.005984518],"genre_scores_gemma":[0.7153832,0.00008497421,0.2828057,0.0004910077,0.00001685561,0.000006820275,0.000002210812,0.000004317411,0.001204839],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9164297,"threshold_uncertainty_score":0.3706191,"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."}}