{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002509567,0.0003728425,0.0002327175,0.002225506,0.0002750813,0.002941877,0.0006967281,0.0005698172,0.0006974912],"category_scores_gemma":[0.01322362,0.0004048671,0.0003301729,0.002903703,0.0005387011,0.002327789,0.0007803311,0.0007672237,0.000279014],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001168415,"about_ca_system_score_gemma":0.000728005,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01076103,"about_ca_topic_score_gemma":0.008663179,"domain_scores_codex":[0.9982133,0.0002061927,0.000128545,0.0004545202,0.0008374734,0.0001599939],"domain_scores_gemma":[0.988466,0.003244557,0.002249256,0.001585677,0.00388343,0.0005710088],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004391689,0.000546046,0.6921064,0.0004377975,0.0003660507,0.0007279353,0.004048749,0.03462137,0.05501997,0.01368953,0.004071988,0.1939251],"study_design_scores_gemma":[0.00002426722,0.0003566514,0.8542844,0.00008972028,0.0001494505,0.00112746,0.002347394,0.07564013,0.03294321,0.0042835,0.0286542,0.00009967194],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9896066,0.0007102154,0.003721052,0.0003729191,0.00001357267,0.00002192862,0.000763101,0.0003317418,0.004458913],"genre_scores_gemma":[0.989595,0.0003747529,0.00693826,0.00008438822,0.000008094399,0.00001276039,0.00121236,0.0001510063,0.001623328],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01076103,"threshold_uncertainty_score":0.02139676,"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."}}