{"id":"W4248175911","doi":"10.32920/ryerson.14662971.v1","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); Condorcet method; Computer science; Metasearch engine; Search engine; Algorithm; Learning to rank; Data mining; Information retrieval; Mathematics; Voting; Engineering; Web search query; 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":[0.002194883,0.0007735784,0.001368728,0.002793699,0.0005526419,0.00271092,0.002499945,0.00121664,0.00391614],"category_scores_gemma":[0.003854411,0.0005236481,0.001298962,0.003091049,0.0002902743,0.002152828,0.001118418,0.001011429,0.002135556],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009192073,"about_ca_system_score_gemma":0.001828435,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002980041,"about_ca_topic_score_gemma":0.001935579,"domain_scores_codex":[0.997991,0.0004241639,0.0002760744,0.0002617127,0.0008140778,0.0002329369],"domain_scores_gemma":[0.9980584,0.0003819679,0.0001011099,0.0005605873,0.000795077,0.0001027686],"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":[0.002742452,0.0008837912,0.0079458,0.001144754,0.0007941963,0.0006243836,0.0003995447,0.09136499,0.05887564,0.04195401,0.02407798,0.7691925],"study_design_scores_gemma":[0.0002983656,0.0005287113,0.002102773,0.00008328646,0.0002622624,0.0008128029,0.0001336967,0.844288,0.1009451,0.01200739,0.03839916,0.0001384511],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0407356,0.001510154,0.9114745,0.0002883465,0.0001622457,0.000405255,0.001486139,0.03634462,0.007593154],"genre_scores_gemma":[0.2067878,0.0005160191,0.7817109,0.000141023,0.00006628297,0.0003252733,0.003999646,0.0007617865,0.005691304],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00391614,"threshold_uncertainty_score":0.0131008,"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."}}