{"id":"W2145664829","doi":"10.1145/1963405.1963459","title":"Learning to rank with multiple objective functions","year":2011,"lang":"en","type":"article","venue":"","topic":"Information Retrieval and Search Behavior","field":"Computer Science","cited_by":54,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Learning to rank; Measure (data warehouse); Computer science; Ranking (information retrieval); Relevance (law); Rank (graph theory); Function (biology); Perspective (graphical); Artificial intelligence; Machine learning; Information retrieval; Data mining; Mathematics","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":[0.01347819,0.002170392,0.003309538,0.003093488,0.0008777174,0.002688062,0.00301935,0.002761672,0.002677571],"category_scores_gemma":[0.02714192,0.000626453,0.001280589,0.003831224,0.00175701,0.004994807,0.002048972,0.003304935,0.001181472],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001997791,"about_ca_system_score_gemma":0.001323677,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002272929,"about_ca_topic_score_gemma":0.002473655,"domain_scores_codex":[0.9947034,0.002734942,0.0002351472,0.0008096955,0.001152671,0.0003641061],"domain_scores_gemma":[0.9792891,0.01484287,0.001691307,0.001795863,0.001882806,0.0004980122],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002361262,0.0004727478,0.003374011,0.0003136015,0.0002573008,0.0001168127,0.0001126568,0.663256,0.001621159,0.03843839,0.004281118,0.28752],"study_design_scores_gemma":[0.00001985624,0.0001385634,0.0003097672,0.000009373592,0.00001976755,0.00003716276,0.00001137747,0.9766223,0.0009863308,0.02124649,0.0005833813,0.00001554376],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.020518,0.0008286887,0.9755988,0.0006220678,0.00005361789,0.00007520136,0.00008221257,0.0005351838,0.001686226],"genre_scores_gemma":[0.3893605,0.0008711499,0.6004012,0.0005033852,0.000531413,0.0003206101,0.0004931049,0.0003417485,0.00717695],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01347819,"threshold_uncertainty_score":0.07128036,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02798282266224064,"score_gpt":0.2342720804439619,"score_spread":0.2062892577817213,"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."}}