{"id":"W3142035783","doi":"10.1109/estmed.2006.321276","title":"Locality management using multiple SPMs on the Multi-Level Computing Architecture","year":2006,"lang":"en","type":"article","venue":"","topic":"Parallel Computing and Optimization Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Locality; Exploit; Scheduling (production processes); Computer architecture; Distributed computing; Data access; Locality of reference; Architecture; Parallel computing; Embedded system; Cache; Database","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":[0.0004111925,0.0002889253,0.0003605723,0.0003408359,0.0005711894,0.0006024104,0.001354842,0.0003018323,0.001429691],"category_scores_gemma":[0.001131109,0.0002264527,0.0003451964,0.0005039292,0.0005047902,0.001296992,0.001079184,0.0007467574,0.000406457],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006689376,"about_ca_system_score_gemma":0.0009249005,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001482312,"about_ca_topic_score_gemma":0.003102592,"domain_scores_codex":[0.9996403,0.0001127805,0.00002372027,0.00005503674,0.0001138777,0.00005415748],"domain_scores_gemma":[0.9990055,0.0002586191,0.0001020617,0.0004265259,0.0001549264,0.00005237267],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0009669004,0.0003788921,0.005018337,0.0003701331,0.0001212626,0.0004136164,0.0007032357,0.2504878,0.2171156,0.0693715,0.0110495,0.4440033],"study_design_scores_gemma":[0.00012197,0.0003711359,0.000935694,0.00002347105,0.00007394074,0.0001867274,0.00006478631,0.8912947,0.07232205,0.01956479,0.01499607,0.00004462143],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1941404,0.0008060535,0.7915875,0.0004493544,0.00006569002,0.00007995874,0.00004059809,0.005445027,0.00738541],"genre_scores_gemma":[0.7317696,0.0001773832,0.263718,0.0001401595,0.0000463359,0.0001133925,0.00006681723,0.0002225691,0.003745808],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001482312,"threshold_uncertainty_score":0.004853547,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05939844105712935,"score_gpt":0.2773955407792227,"score_spread":0.2179970997220933,"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."}}