{"id":"W2998178972","doi":"10.1109/tit.2019.2962495","title":"Optimization of Heterogeneous Coded Caching","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Information Theory","topic":"Caching and Content Delivery","field":"Computer Science","cited_by":47,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Defence Research and Development Canada","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Cache; Optimization problem; Scheme (mathematics); Function (biology); Mathematical optimization; CPU cache; Perspective (graphical); Encoding (memory); Distributed computing; Algorithm; Parallel computing; Mathematics","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":[0.0002731689,0.00008408005,0.000101336,0.0002140396,0.00008576251,0.0000602129,0.0002609146,0.00004793588,0.00007609859],"category_scores_gemma":[0.000003799045,0.00008111819,0.00008468643,0.0001709612,0.00001574231,0.00140482,0.000001900229,0.0001124772,0.000170215],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003397117,"about_ca_system_score_gemma":0.00002956208,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001302508,"about_ca_topic_score_gemma":6.667282e-7,"domain_scores_codex":[0.9992754,0.00006937744,0.0002710303,0.00008701436,0.0001930591,0.0001040492],"domain_scores_gemma":[0.9993376,0.00009530294,0.0001217009,0.000327689,0.00008421744,0.00003353768],"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":[0.00003148801,0.00002332753,0.00000194827,0.00001436607,0.0000162967,1.434626e-7,0.0006348875,0.9724245,0.0003034802,0.005962636,0.000007368089,0.02057952],"study_design_scores_gemma":[0.0004522523,0.0001090917,0.000005957914,0.00003194081,0.000008651497,0.00001441134,0.0001009096,0.9816397,0.01692877,0.0004852702,0.00009567384,0.0001273655],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02284961,0.000007237732,0.9740952,0.00003880312,0.0006275452,0.0001418758,0.000009071839,0.0001331778,0.002097509],"genre_scores_gemma":[0.9971939,0.000009138001,0.002283949,0.0003179901,0.000004911818,0.000007994023,0.000003432823,0.000003887757,0.000174796],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9743443,"threshold_uncertainty_score":0.3307904,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006795102457840474,"score_gpt":0.1968553848828865,"score_spread":0.1900602824250461,"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."}}