{"id":"W2162079228","doi":"10.1109/cec.2006.1688493","title":"An Evolutionary Approach to Optimal Web Proxy Cache Placement","year":2006,"lang":"en","type":"article","venue":"","topic":"Caching and Content Delivery","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Cache; Proxy (statistics); Evolutionary programming; Network packet; Evolutionary algorithm; Genetic programming; Distributed computing; Evolutionary computation; Cache algorithms; CPU cache; Parallel computing; Computer network; Artificial intelligence; Machine learning","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001679866,0.000103145,0.00008348091,0.00008230614,0.0001146174,0.0001205477,0.0005522308,0.00003272709,0.00001013797],"category_scores_gemma":[0.00000307462,0.00008872237,0.00004192604,0.0001907521,0.0000127894,0.0003183825,0.0001463639,0.00007400561,0.00008992066],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006561822,"about_ca_system_score_gemma":0.00006443558,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003330496,"about_ca_topic_score_gemma":0.000005338004,"domain_scores_codex":[0.9989438,0.00004095408,0.0001372709,0.0003771867,0.000262972,0.000237806],"domain_scores_gemma":[0.9993883,0.00001158011,0.00002054782,0.0004370304,0.00004679718,0.00009578247],"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":[0.00007121572,0.002827314,0.005074339,0.00002271264,0.00004362886,0.0000292202,0.0009646015,0.4152359,0.02295136,0.3680311,0.178676,0.006072552],"study_design_scores_gemma":[0.0002230839,0.0001269115,0.001626875,0.000003540584,0.000002901182,0.00001945465,0.00009091098,0.9937493,0.0001771673,0.0001438205,0.003654679,0.0001813573],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1466116,0.0000608345,0.7932308,0.0005773253,0.0001524758,0.0002458886,0.0000026393,0.0003930449,0.05872545],"genre_scores_gemma":[0.8560876,4.265283e-7,0.1398635,0.0003922021,0.0001068499,0.00003382006,0.000009328831,0.000004938739,0.003501386],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7094759,"threshold_uncertainty_score":0.3617993,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01321761441112543,"score_gpt":0.2182866421368221,"score_spread":0.2050690277256967,"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."}}