{"id":"W2952813088","doi":"10.1002/smr.1915","title":"Database engines: Evolution of greenness","year":2017,"lang":"en","type":"article","venue":"Journal of Software Evolution and Process","topic":"Green IT and Sustainability","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"IBM (Canada); Toronto Metropolitan University","funders":"","keywords":"Database; Computer science; Energy consumption; Metric (unit); Consumption (sociology); Energy (signal processing); Real-time database; Efficient energy use","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":[0.00369062,0.0003931352,0.0002412749,0.002835409,0.0003446527,0.002581306,0.0007022966,0.000444269,0.0006725165],"category_scores_gemma":[0.02282294,0.0003634835,0.0003501097,0.002943883,0.0005342282,0.002162785,0.0007748872,0.0007129918,0.0001989593],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001392417,"about_ca_system_score_gemma":0.0006936603,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009631105,"about_ca_topic_score_gemma":0.007914286,"domain_scores_codex":[0.9973103,0.0005333852,0.0002444318,0.0004946954,0.001204606,0.000212583],"domain_scores_gemma":[0.9687191,0.01148426,0.006894454,0.002977845,0.00896286,0.0009616126],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002092905,0.0002907512,0.9034558,0.0001429072,0.0001991178,0.0004187774,0.001654383,0.01716562,0.01633867,0.002433567,0.001080795,0.05661035],"study_design_scores_gemma":[0.0000111597,0.0002490021,0.9152313,0.00004384096,0.0000830522,0.0005941666,0.001561478,0.06511831,0.01115095,0.001541708,0.0043678,0.00004732682],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9958086,0.0002017684,0.001852255,0.0001407313,0.000004946834,0.00001460197,0.0002513529,0.0001267961,0.001598874],"genre_scores_gemma":[0.9971136,0.00006618261,0.002058536,0.00002220145,0.000002457988,0.000006307572,0.0003491924,0.00003213089,0.0003493236],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009631105,"threshold_uncertainty_score":0.01951808,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008740486068826665,"score_gpt":0.2429144581907307,"score_spread":0.2341739721219041,"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."}}