{"id":"W1985490796","doi":"10.1145/1982595.1982614","title":"Selection and execution of user level test cases for energy cost evaluation of smartphones","year":2011,"lang":"en","type":"article","venue":"","topic":"Green IT and Sustainability","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Test (biology); Selection (genetic algorithm); Energy (signal processing); Work (physics); Scenario testing; Embedded system; Engineering; Machine learning; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002132387,0.000044641,0.00007279526,0.00004132709,0.00001479062,0.000001760939,0.00001545654,0.00003556022,0.00004336315],"category_scores_gemma":[0.0001261211,0.0000412349,0.00001734261,0.00006303914,0.00001545474,0.00007390705,0.000004167842,0.00001157822,9.3007e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003352985,"about_ca_system_score_gemma":0.00001775646,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007528055,"about_ca_topic_score_gemma":0.001403688,"domain_scores_codex":[0.9996824,0.00001161887,0.0001181159,0.00005605824,0.00007194597,0.00005989231],"domain_scores_gemma":[0.9995529,0.00006371624,0.00002023984,0.00005181066,0.0002972596,0.00001407013],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002858411,0.0006799293,0.5533366,0.001678701,0.0001902123,7.454616e-7,0.003611,0.005333158,0.1486071,0.01565326,0.00338064,0.2672428],"study_design_scores_gemma":[0.0006956779,0.00022781,0.387047,0.00001202947,0.00008824607,0.000005513524,0.0005366182,0.2426831,0.3661137,0.002173016,0.0002962371,0.0001210927],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9738608,0.0000350657,0.02510145,0.000002928339,0.00004461291,0.0001758393,0.00000786996,0.0000246915,0.0007467333],"genre_scores_gemma":[0.9989904,0.000005608998,0.0008669775,0.000001309417,0.000009435757,0.00003498185,0.0000055885,0.000005284031,0.00008048216],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2671217,"threshold_uncertainty_score":0.1681511,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09590530263328696,"score_gpt":0.2634202754029892,"score_spread":0.1675149727697023,"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."}}