{"id":"W2008164459","doi":"10.1109/tetc.2015.2398824","title":"Data Allocation for Hybrid Memory With Genetic Algorithm","year":2015,"lang":"en","type":"article","venue":"IEEE Transactions on Emerging Topics in Computing","topic":"Big Data and Digital Economy","field":"Computer Science","cited_by":100,"is_retracted":false,"has_abstract":true,"ca_institutions":"St. Francis Xavier University","funders":"Division of Computer and Network Systems; National Natural Science Foundation of China; National Science Foundation","keywords":"Computer science; Cache-only memory architecture; Parallel computing; Interleaved memory; Bottleneck; Uniform memory access; Flat memory model; Memory architecture; Static random-access memory; Memory management; Registered memory; Semiconductor memory; Latency (audio); Non-uniform memory access; Embedded system; Computer architecture; Computer hardware","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":[0.0003329361,0.0005256197,0.0006212246,0.000689373,0.0004138265,0.0007625954,0.001263755,0.0008780248,0.001805654],"category_scores_gemma":[0.0009127567,0.0002266501,0.0004425175,0.0007471082,0.0003790896,0.0006414014,0.0006021983,0.0003664383,0.0002219099],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006439632,"about_ca_system_score_gemma":0.0008769117,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006915258,"about_ca_topic_score_gemma":0.00505366,"domain_scores_codex":[0.9998108,0.00004142533,0.00001040229,0.00005037701,0.00004602654,0.00004108731],"domain_scores_gemma":[0.9997484,0.0001242476,0.00002413487,0.00002109316,0.00006835353,0.00001369619],"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":[0.0001161319,0.00008963765,0.0009786279,0.00005332617,0.00005482503,0.00008090521,0.00006061676,0.8851178,0.004242801,0.005938515,0.001194821,0.1020721],"study_design_scores_gemma":[0.00001471819,0.00001838918,0.00005622047,0.000002041622,0.000006807399,0.00001201965,0.000006642505,0.9981883,0.0005179371,0.000921118,0.0002531609,0.000002696874],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1223355,0.0006875256,0.8686375,0.0002957503,0.00007831485,0.00009293221,0.0000658245,0.00115557,0.006650991],"genre_scores_gemma":[0.7399202,0.0002306323,0.2548471,0.0002042732,0.00002965654,0.0002767669,0.0001326282,0.000062705,0.004296052],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006915258,"threshold_uncertainty_score":0.01375002,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08209833086676656,"score_gpt":0.2968735042650074,"score_spread":0.2147751733982408,"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."}}