{"id":"W1616993132","doi":"10.1109/tkde.2015.2448541","title":"A Family of Rank Similarity Measures Based on Maximized Effectiveness Difference","year":2015,"lang":"en","type":"article","venue":"IEEE Transactions on Knowledge and Data Engineering","topic":"Information Retrieval and Search Behavior","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Chinese Academy of Sciences; Google","keywords":"Relevance (law); Measure (data warehouse); Metric (unit); Similarity (geometry); Rank (graph theory); Computer science; Similarity measure; Learning to rank; Maximization; Context (archaeology); Ranking (information retrieval); Data mining; Information retrieval; Mathematics; Artificial intelligence; Mathematical optimization","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005728825,0.0001315979,0.0001875217,0.0001956349,0.00006096488,0.00005739709,0.0005138473,0.00005590558,0.000002127619],"category_scores_gemma":[0.00003405061,0.0001132342,0.00003589792,0.0003069698,0.00002430107,0.0004189875,0.00001080105,0.0001848422,0.00001026019],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003260457,"about_ca_system_score_gemma":0.00009292023,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001681709,"about_ca_topic_score_gemma":0.000002851691,"domain_scores_codex":[0.9991194,0.000062968,0.0001736222,0.0002351238,0.0002530983,0.0001557726],"domain_scores_gemma":[0.9988269,0.0002449723,0.00002561158,0.0006265047,0.0001424576,0.0001335776],"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":[0.001784288,0.002744818,0.0003183552,0.001451527,0.0002091804,0.00003462362,0.003159897,0.2413363,0.03881082,0.002826381,0.0004962571,0.7068275],"study_design_scores_gemma":[0.001405632,0.0001889944,0.002646789,0.000101615,0.00001542194,0.00000209297,0.000008524449,0.9719421,0.02317284,0.00001911996,0.0003418208,0.000155056],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02533409,0.0000618363,0.9737015,0.00001883859,0.0003422286,0.0001680361,0.00008551386,0.00009794514,0.0001900007],"genre_scores_gemma":[0.9957936,0.0000140846,0.004117541,0.00001917172,0.00001017056,0.00001679668,0.000008355867,0.000006260536,0.00001402996],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9704595,"threshold_uncertainty_score":0.4617555,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07556383053037817,"score_gpt":0.2933464697698414,"score_spread":0.2177826392394632,"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."}}