{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001076175,0.00005311563,0.00004956354,0.00008747909,0.0001544484,0.00005163485,0.0002017447,0.0000167141,0.0001770034],"category_scores_gemma":[0.00003128631,0.00003598621,0.00001878094,0.0003741072,0.00001338935,0.0005030555,0.0000791043,0.00009520283,0.0008364824],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001750419,"about_ca_system_score_gemma":0.00003621958,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001323861,"about_ca_topic_score_gemma":0.0000338141,"domain_scores_codex":[0.9994763,0.00001841408,0.00007688125,0.0001133065,0.0001621606,0.0001529635],"domain_scores_gemma":[0.9995801,0.00002544815,0.00001755729,0.000145542,0.0001373329,0.00009405617],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.000596776,0.0005870826,0.2336237,0.00002721189,0.0001101521,0.00006344467,0.1940706,0.001884981,0.002786305,0.09554356,0.005172526,0.4655337],"study_design_scores_gemma":[0.002820812,0.004848082,0.8094293,0.00003511319,0.0000202185,0.00008375565,0.009675405,0.08431771,0.04459678,0.0003045846,0.04278858,0.001079699],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1062339,7.14305e-7,0.8351648,0.00008854316,0.00007239264,0.0001467302,4.15776e-7,0.0002026582,0.05808976],"genre_scores_gemma":[0.951319,2.434075e-7,0.04104467,0.00021051,0.000008979457,0.00001839363,7.850804e-7,0.000002492728,0.007394905],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8450851,"threshold_uncertainty_score":0.9999415,"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."}}