{"id":"W4392906086","doi":"10.32920/25412869.v1","title":"Power-aware Future Computing Systems Using Machine Learning Techniques","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Parallel Computing and Optimization Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Graphics processing unit; Throughput; Cache; Multi-core processor; Embedded system; Computer architecture; Energy consumption; Computer engineering; Distributed computing; Parallel computing; Operating system","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0009197735,0.0006066192,0.0006540711,0.0005891429,0.000339408,0.001734275,0.001907981,0.000619106,0.00001110048],"category_scores_gemma":[0.00002547172,0.000552538,0.0002597701,0.0005716034,0.00004038726,0.0001397362,0.007328979,0.002308392,0.00002639032],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002694988,"about_ca_system_score_gemma":0.0002546603,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003583588,"about_ca_topic_score_gemma":0.000002336084,"domain_scores_codex":[0.9966596,0.000307356,0.0007339408,0.001271463,0.0005149614,0.0005126214],"domain_scores_gemma":[0.998013,0.00008257666,0.0004218998,0.001050469,0.0002935761,0.0001384748],"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":[0.00001135688,0.0001936539,0.0007772294,0.00319919,0.0004815956,0.0004220963,0.003608475,0.8099649,0.0006196322,0.1413837,0.01179565,0.02754256],"study_design_scores_gemma":[0.00004154143,0.00004755045,0.000003946899,0.0009764344,0.00001672711,0.00009640283,0.00004221461,0.9877174,0.00115737,0.002107841,0.007152935,0.0006396088],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0007008513,0.004470916,0.9730467,0.0005745253,0.002788164,0.0005436082,0.000007654389,0.01197091,0.005896654],"genre_scores_gemma":[0.3148531,0.000167383,0.6827986,0.0001965321,0.0008478057,0.00001711212,0.00003762462,0.0000895474,0.0009922715],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3141523,"threshold_uncertainty_score":0.9999933,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01833690374576704,"score_gpt":0.2842415069271517,"score_spread":0.2659046031813846,"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."}}