{"id":"W2117039411","doi":"10.1109/ccece.2007.131","title":"Investigating Cache Energy Efficiency in Multimedia Processors","year":2007,"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 Victoria","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Cache; Cache algorithms; Computer science; Smart Cache; Cache invalidation; Cache pollution; Cache coloring; Page cache; Parallel computing; Latency (audio); Efficient energy use; CPU cache; Operating system; Telecommunications","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003627258,0.0004023379,0.0003807033,0.0007293018,0.0003001272,0.0005503912,0.0007482925,0.0005157037,0.001137653],"category_scores_gemma":[0.003911315,0.0002308778,0.0002303209,0.001334401,0.0002995709,0.0018166,0.000341063,0.0003157029,0.0002102817],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006734882,"about_ca_system_score_gemma":0.0003291932,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00201472,"about_ca_topic_score_gemma":0.002405698,"domain_scores_codex":[0.9996917,0.00005639887,0.00001098614,0.00003536164,0.0001494871,0.00005618971],"domain_scores_gemma":[0.9984585,0.001072601,0.0001624269,0.00008317598,0.0002011401,0.00002211656],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0006153116,0.0001647966,0.02040904,0.0003554407,0.0001094494,0.0003404085,0.0001505679,0.8492978,0.0617266,0.00808328,0.0007413427,0.05800591],"study_design_scores_gemma":[0.00003384587,0.0003931854,0.009313982,0.00002656,0.00005031108,0.0001818585,0.0001149374,0.9184117,0.06337283,0.006022148,0.00204852,0.00003006335],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9499675,0.00181413,0.04384319,0.000163536,0.000009363634,0.0000272591,0.0001128398,0.0001347153,0.003927458],"genre_scores_gemma":[0.9894058,0.0004848306,0.009142194,0.00002729975,0.000007862734,0.00002050066,0.0001335649,0.00004012027,0.0007377595],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00201472,"threshold_uncertainty_score":0.004886508,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01748968084466255,"score_gpt":0.266048962900799,"score_spread":0.2485592820561365,"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."}}