{"id":"W1903488793","doi":"10.1109/tkde.2015.2453952","title":"A Cooperative Coevolution Framework for Parallel Learning to Rank","year":2015,"lang":"en","type":"article","venue":"IEEE Transactions on Knowledge and Data Engineering","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Natural Science Foundation of Shandong Province; Academy of Finland; National Natural Science Foundation of China; National Science Foundation","keywords":"Computer science; Benchmark (surveying); Rank (graph theory); Coevolution; Context (archaeology); Learning to rank; Artificial intelligence; Divide and conquer algorithms; Machine learning; Evolutionary algorithm; Function (biology); Theoretical computer science; Algorithm; Ranking (information retrieval); 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":[],"consensus_categories":[],"category_scores_codex":[0.0001976847,0.0001169936,0.0001127694,0.0001020004,0.0002036499,0.0000692002,0.0003838449,0.00005643896,0.000001948558],"category_scores_gemma":[0.0000313116,0.0001197916,0.00002226387,0.0003415087,0.00001224223,0.0004422978,0.00001360334,0.0001779294,0.00004380043],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004257848,"about_ca_system_score_gemma":0.00005732877,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006332608,"about_ca_topic_score_gemma":0.000005948708,"domain_scores_codex":[0.9992255,0.00001570828,0.0001324072,0.0003669578,0.0000796946,0.0001797377],"domain_scores_gemma":[0.9990847,0.0001798882,0.00001670148,0.0004367022,0.0001024248,0.0001795725],"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":[0.0000350104,0.0002779614,0.000005276499,0.00004096758,0.00006426608,0.000001375099,0.002774772,0.873185,0.0004935424,0.06173203,0.003670018,0.05771983],"study_design_scores_gemma":[0.0003342475,0.0001393487,0.00003266974,0.00003837306,0.00001070206,0.000007234984,0.00006150585,0.9618369,0.0002582773,0.0004653873,0.03664723,0.0001681118],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0003473158,0.0002902785,0.9980493,0.0004059543,0.0003260139,0.000282237,0.0000564966,0.0001866583,0.00005576314],"genre_scores_gemma":[0.4016569,0.0000495786,0.5975772,0.00004849678,0.0001266069,0.0002361957,0.00002793944,0.00001550963,0.0002616546],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4013095,"threshold_uncertainty_score":0.488496,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04245486072800539,"score_gpt":0.3010015428149192,"score_spread":0.2585466820869138,"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."}}