{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003799842,0.0001065097,0.0002216678,0.0001228622,0.0001476052,0.00003197911,0.0002234089,0.00007237145,0.0000138642],"category_scores_gemma":[0.0005031812,0.00009313433,0.000059741,0.00007644606,0.0001018204,0.000765208,0.00002954334,0.0001775107,9.128961e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001117555,"about_ca_system_score_gemma":0.000104024,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003698156,"about_ca_topic_score_gemma":0.00002310232,"domain_scores_codex":[0.9992165,0.00001495995,0.0003403526,0.00007970898,0.000205205,0.0001432803],"domain_scores_gemma":[0.9988829,0.00003727288,0.0002503589,0.0002156689,0.0005193489,0.00009445107],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002476324,0.0001632004,0.9560043,0.004664897,0.0001548204,0.00003968591,0.001247401,0.00650761,0.001131952,0.001752165,0.001105141,0.02698123],"study_design_scores_gemma":[0.001853049,0.0002219474,0.9676565,0.0005600242,0.0001257951,0.0002252243,0.00154858,0.01150456,0.001203423,0.01372848,0.000961313,0.0004110908],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.840049,0.002563187,0.1567356,0.00005333435,0.0003735195,0.00007069009,0.00001727368,0.00003770716,0.00009968569],"genre_scores_gemma":[0.9989815,0.00006770591,0.0007759852,0.000002256141,0.0001316321,0.000001608544,0.000001429273,0.00001093365,0.00002692577],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1589325,"threshold_uncertainty_score":0.3797908,"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."}}