{"id":"W4365441266","doi":"10.48550/arxiv.2304.04954","title":"An Associativity Threshold Phenomenon in Set-Associative Caches","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Caching and Content Delivery","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; National Science Foundation","keywords":"Cache; Computer science; Cache algorithms; Paging; Parallel computing; Hash function; Associative property; Cache pollution; Page cache; Cache coloring; CPU cache; Combinatorics; Mathematics; Computer network","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008939554,0.0003680805,0.0005033399,0.0004229938,0.0001782266,0.0002228094,0.002088722,0.0003936126,0.000005732181],"category_scores_gemma":[0.00006226711,0.0004556827,0.0002297159,0.0008523759,0.00006178534,0.000674957,0.001772569,0.001087253,0.000109262],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001096765,"about_ca_system_score_gemma":0.0002535843,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002419043,"about_ca_topic_score_gemma":0.001994293,"domain_scores_codex":[0.9972322,0.0003908907,0.0002411533,0.001411331,0.0001814217,0.0005429809],"domain_scores_gemma":[0.9979506,0.0002494317,0.0003494722,0.001156463,0.0001281366,0.0001659422],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007594359,0.0008385394,0.5122413,0.0001310169,0.0004334893,0.001907526,0.00844832,0.326812,0.0002807985,0.146495,0.001012089,0.001323971],"study_design_scores_gemma":[0.0007988393,0.0001081305,0.08889195,0.0001346138,0.00005742723,6.532476e-7,0.0007191001,0.8114948,0.0000350589,0.0968378,0.00002932527,0.0008922982],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.956171,0.00004035053,0.03992657,0.0002303765,0.0007576003,0.0002844122,0.00006804157,0.0006294313,0.001892189],"genre_scores_gemma":[0.9974995,0.00009897345,0.00008608544,0.0001075465,0.0000836152,0.000002290289,0.00003880392,0.0000264251,0.0020568],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4846828,"threshold_uncertainty_score":0.9997895,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1623922495440452,"score_gpt":0.2156655185607227,"score_spread":0.0532732690166775,"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."}}