{"id":"W4246628364","doi":"10.32920/ryerson.14662971","title":"Implementation of meta search engine with different rank aggregation algorithms","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Fuse (electrical); Rank (graph theory); Ranking (information retrieval); Computer science; Condorcet method; Search engine; Metasearch engine; Algorithm; Learning to rank; Data mining; Information retrieval; Mathematics; Voting; Web search query; Engineering","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":[0.0003686292,0.0002856761,0.0004843625,0.000239154,0.00004658831,0.000437762,0.001108404,0.00006853646,0.0003258195],"category_scores_gemma":[0.0000035994,0.0002022162,0.0001656223,0.0003214657,0.00002941437,0.0005676342,0.002221452,0.0002356465,0.000003485851],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004511913,"about_ca_system_score_gemma":0.0001001166,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006642696,"about_ca_topic_score_gemma":0.0001815178,"domain_scores_codex":[0.997811,0.0001056456,0.0003857354,0.0006959928,0.0007459059,0.0002557679],"domain_scores_gemma":[0.998382,0.00004433608,0.0002000094,0.001084717,0.0002221777,0.00006675484],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000009352958,0.0002838717,0.000755669,0.0006564337,0.004089953,0.00004950413,0.001209985,0.003089152,0.0001546428,0.01102641,0.00067536,0.9779997],"study_design_scores_gemma":[0.003323213,0.000542481,0.01542637,0.0003050813,0.002270455,0.00001400145,0.001830419,0.8575112,0.114708,0.001483351,0.0008655086,0.001719967],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02598356,0.0002521315,0.9718499,0.0006370479,0.0002990115,0.0005388334,0.00003276422,0.0001147974,0.0002919211],"genre_scores_gemma":[0.4223211,0.0005281992,0.5737457,0.0001392804,0.000158944,0.0002127344,0.001589487,0.00003672273,0.001267747],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9762797,"threshold_uncertainty_score":0.8246137,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04202704840148233,"score_gpt":0.3018620907241171,"score_spread":0.2598350423226348,"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."}}