{"id":"W4247450063","doi":"10.32920/ryerson.14647074","title":"RankGPES: learning to rank for information retrieval using a hybrid genetic programming with evolutionary strategies","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Rank (graph theory); Genetic programming; Computer science; Ranking (information retrieval); Learning to rank; Artificial intelligence; Evolutionary algorithm; Machine learning; Evolutionary programming; Genetic algorithm; 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":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0007580426,0.0003241556,0.000405473,0.0005468512,0.0003265737,0.002897077,0.0009619639,0.0001337689,0.00004124994],"category_scores_gemma":[0.0006088214,0.0002995461,0.000118379,0.0008232205,0.00005508161,0.001304755,0.001282551,0.0005600607,0.00001200749],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002387716,"about_ca_system_score_gemma":0.002331536,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008456064,"about_ca_topic_score_gemma":0.000003183069,"domain_scores_codex":[0.9968925,0.0002104682,0.0006256392,0.0006732663,0.001054501,0.0005436672],"domain_scores_gemma":[0.9968863,0.0001960602,0.0002791999,0.0007019073,0.001717742,0.000218765],"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":[0.00007422773,0.00003440132,0.00003642773,0.0004061564,0.00008123077,0.00001706078,0.001116834,0.972645,0.00003452176,0.0007780245,0.00007131972,0.02470482],"study_design_scores_gemma":[0.0005372643,0.0001744938,0.0001185032,0.0001817694,0.00002675582,0.00006729873,0.0007874393,0.9950483,0.0002224502,0.0001880486,0.002251083,0.0003966013],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005541907,0.0001540998,0.9910781,0.0002337119,0.0003214596,0.002069423,0.000009154292,0.0002991766,0.000292972],"genre_scores_gemma":[0.03111358,0.00002587559,0.9682161,0.00007521845,0.0001016914,0.0001013723,0.0001554129,0.00002468967,0.000186078],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02557167,"threshold_uncertainty_score":0.9999456,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02839525941810875,"score_gpt":0.2993532365904498,"score_spread":0.270957977172341,"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."}}