{"id":"W4249677314","doi":"10.32920/ryerson.14666424","title":"Software energy consumption prediction using software code metrics","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Green IT and Sustainability","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Energy consumption; Computer science; Software; Efficient energy use; Consumption (sociology); Product (mathematics); Energy accounting; Energy (signal processing); Source code; Reliability engineering; Engineering; Operating system; Statistics","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"],"consensus_categories":[],"category_scores_codex":[0.0002032977,0.0003594342,0.0004142377,0.0002904346,0.00009065196,0.0001606211,0.0001982036,0.0006562471,0.0004124319],"category_scores_gemma":[0.0002940067,0.0004038544,0.0002065976,0.0002833808,0.00004292133,0.0001507476,0.0003736259,0.0005636839,0.000004415997],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008122528,"about_ca_system_score_gemma":0.0001638646,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003396843,"about_ca_topic_score_gemma":0.0002135655,"domain_scores_codex":[0.998317,0.00006372092,0.0004422976,0.0004928481,0.0003315132,0.0003525959],"domain_scores_gemma":[0.9987185,0.0001084912,0.00006467207,0.000644042,0.0003379579,0.0001263075],"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":[0.00001142317,0.00008237788,0.1669821,0.0036681,0.000351878,0.00006243957,0.0003390915,0.7857318,0.0001578012,0.0001000114,0.00181315,0.04069985],"study_design_scores_gemma":[0.0006673517,0.00003562845,0.03876586,0.0004505976,0.0004644177,0.00005078482,0.0005472658,0.9465791,0.003244265,0.002679138,0.004833167,0.001682409],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2487086,0.001650748,0.746218,0.000005407816,0.001564711,0.000155259,0.000120632,0.001425999,0.0001505523],"genre_scores_gemma":[0.9329,0.0005630853,0.06490055,0.00003730407,0.0002701938,0.00005533857,0.0008313381,0.0001133241,0.0003289096],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6841913,"threshold_uncertainty_score":0.9998413,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03141875065618977,"score_gpt":0.2454110182123042,"score_spread":0.2139922675561144,"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."}}