{"id":"W4240716022","doi":"10.32920/ryerson.14663997","title":"Database engines: evolution of greenness","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":"Database; Energy consumption; Computer science; Metric (unit); Software; Energy (signal processing); Efficient energy use; Consumption (sociology); Relation (database); Electricity; Real-time database; Operating system; Engineering; Operations management","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":[0.0001552661,0.0001856569,0.0003000126,0.00009417442,0.0000121685,0.00001701517,0.0001965126,0.0001927516,0.0002270726],"category_scores_gemma":[0.00005875396,0.0001902997,0.0001218325,0.0001341034,0.00002820306,0.00007907314,0.0003294442,0.0003250547,0.000003565008],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002072837,"about_ca_system_score_gemma":0.0001209462,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008061796,"about_ca_topic_score_gemma":0.0003410057,"domain_scores_codex":[0.999141,0.00002297961,0.0002812154,0.000230455,0.0001481451,0.0001761839],"domain_scores_gemma":[0.9989758,0.00003040918,0.00002821402,0.0007247998,0.000187857,0.00005292357],"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":[0.00004626487,0.0005549418,0.08355366,0.05735033,0.001047242,0.0002140214,0.003111274,0.7933422,0.01155855,0.02010191,0.006700622,0.02241899],"study_design_scores_gemma":[0.0009480855,0.0000420564,0.09392394,0.001012469,0.0003028261,0.00001883248,0.008125546,0.864728,0.02055275,0.006177581,0.00191929,0.002248617],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7885752,0.001518353,0.2039092,0.00001960619,0.0008480256,0.0002274221,0.00006420939,0.0003268628,0.004511106],"genre_scores_gemma":[0.9975424,0.00004089385,0.001959794,0.000001833442,0.0000722828,0.00002247912,0.0001834927,0.00002391619,0.000152867],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2089673,"threshold_uncertainty_score":0.7760195,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009127680249968628,"score_gpt":0.2111877777329655,"score_spread":0.2020600974829969,"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."}}