{"id":"W4282047953","doi":"10.1287/mnsc.2022.4442","title":"Green Cloud? An Empirical Analysis of Cloud Computing and Energy Efficiency","year":2022,"lang":"en","type":"article","venue":"Management Science","topic":"Green IT and Sustainability","field":"Engineering","cited_by":76,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Cloud computing; Efficient energy use; Vendor; Computer science; Cloud testing; Software as a service; Green computing; Cloud computing security; Environmental economics; Software; Business; Economics; Engineering; Marketing; Operating system; Software development","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":[0.0008958001,0.00007938945,0.0001489069,0.0004519962,0.0002997431,0.00003079678,0.0004277393,0.000009243902,0.00004168375],"category_scores_gemma":[0.000007144216,0.00008153811,0.00004083293,0.003039428,0.0002049817,0.00009632626,0.0004932109,0.00006003465,2.876696e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001018315,"about_ca_system_score_gemma":0.00000895954,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001509469,"about_ca_topic_score_gemma":0.00002703671,"domain_scores_codex":[0.9988126,0.00003649788,0.0001779376,0.0002876188,0.0004259503,0.0002594422],"domain_scores_gemma":[0.999548,0.00002134849,0.00002830188,0.0003063844,0.00002502982,0.00007099919],"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":[0.000008527483,0.0001512293,0.1671312,0.0001097849,0.0001561984,0.00002416347,0.00272029,0.7575774,0.0002343322,0.01587726,0.000206513,0.05580309],"study_design_scores_gemma":[0.00007834988,0.00004708068,0.1458523,9.830526e-7,0.00008593103,5.292503e-7,0.001478594,0.8504027,0.00002876639,0.0002905681,0.001630457,0.0001038339],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9745103,0.00004314606,0.02054222,0.00004790175,0.0001686342,0.00005011735,0.000002553484,0.00007532816,0.004559847],"genre_scores_gemma":[0.9994262,0.000002651339,0.0003640245,0.00005071974,0.00001470853,0.000004150135,0.00000244484,0.00000437793,0.000130764],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09282523,"threshold_uncertainty_score":0.3325028,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01029689421457102,"score_gpt":0.2543286193654958,"score_spread":0.2440317251509247,"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."}}