{"id":"W4313413552","doi":"10.1109/sec54971.2022.00016","title":"Characterizing Variability in Heterogeneous Edge Systems: A Methodology & Case Study","year":2022,"lang":"en","type":"article","venue":"","topic":"Parallel Computing and Optimization Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Node (physics); Software deployment; Enhanced Data Rates for GSM Evolution; Compiler; Power consumption; Limit (mathematics); Distributed computing; Power (physics); Software engineering; Programming language; Artificial intelligence","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":[0.00421147,0.0005753954,0.0003927574,0.001003687,0.001121732,0.001588299,0.001382575,0.001436814,0.0009837192],"category_scores_gemma":[0.009773267,0.0003056297,0.00064889,0.001541207,0.001394966,0.00113542,0.00131593,0.001128951,0.0001951007],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001032952,"about_ca_system_score_gemma":0.0009095187,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003961331,"about_ca_topic_score_gemma":0.004687824,"domain_scores_codex":[0.9958597,0.002218853,0.000193725,0.0005115714,0.0008674149,0.0003486907],"domain_scores_gemma":[0.9879022,0.007630603,0.000908586,0.001889463,0.001406383,0.0002628483],"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":[0.0006413182,0.002057903,0.112248,0.001164038,0.0003095394,0.006983927,0.007584075,0.4808388,0.0398674,0.08440252,0.00787393,0.2560286],"study_design_scores_gemma":[0.0001500286,0.001355812,0.02718508,0.0002808291,0.0002114853,0.003360282,0.007849828,0.8289516,0.04843643,0.04939804,0.03264127,0.0001793699],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.511504,0.0004974493,0.4740109,0.001016769,0.00005834364,0.0008122604,0.000530181,0.0003634438,0.01120666],"genre_scores_gemma":[0.808729,0.0002643614,0.188495,0.0001644168,0.00003126944,0.0003862419,0.0002013488,0.00008622966,0.001642245],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00421147,"threshold_uncertainty_score":0.02227265,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09938481688554733,"score_gpt":0.3394388516707659,"score_spread":0.2400540347852186,"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."}}