{"id":"W1859306681","doi":"10.1609/aaai.v25i1.8078","title":"CCRank: Parallel Learning to Rank with Cooperative Coevolution","year":2011,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Data Stream Mining Techniques","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Texas State University","keywords":"Computer science; Rank (graph theory); Coevolution; Context (archaeology); Divide and conquer algorithms; Learning to rank; Function (biology); Space (punctuation); Artificial intelligence; Function optimization; Machine learning; Theoretical computer science; Algorithm; Ranking (information retrieval); Mathematics; Genetic algorithm; Combinatorics","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":[0.003533723,0.001433449,0.002292772,0.001372641,0.0009312055,0.00211103,0.002911352,0.002056909,0.004651854],"category_scores_gemma":[0.01144113,0.0006318956,0.0009911252,0.001688122,0.00171122,0.002514112,0.002674159,0.00221445,0.001907695],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000863097,"about_ca_system_score_gemma":0.003018294,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004342456,"about_ca_topic_score_gemma":0.006099039,"domain_scores_codex":[0.9980262,0.0005541784,0.0001167962,0.0003355473,0.0007161173,0.0002510926],"domain_scores_gemma":[0.9961048,0.001459434,0.0002808617,0.0008719896,0.001041362,0.0002415481],"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":[0.000312909,0.0002919864,0.001637847,0.0002036947,0.0001563463,0.0001480629,0.000140779,0.5914372,0.004542838,0.03077455,0.01078104,0.3595728],"study_design_scores_gemma":[0.00004874637,0.00006874091,0.00006407412,0.00000711197,0.00001271837,0.00004088459,0.00001132068,0.9878559,0.001060295,0.009204544,0.001614852,0.00001090247],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01445388,0.0003900784,0.9790747,0.0003510866,0.0001424939,0.0001540601,0.00006922552,0.001930859,0.003433554],"genre_scores_gemma":[0.2804735,0.0003280807,0.7097917,0.0005830165,0.0002045847,0.0005418207,0.000449829,0.0004784922,0.007149064],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004651854,"threshold_uncertainty_score":0.01868832,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09218986205550374,"score_gpt":0.2857508085373353,"score_spread":0.1935609464818316,"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."}}