{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004079568,0.000322431,0.0005243709,0.002744676,0.0009078334,0.002769415,0.0009588174,0.001103068,0.004025471],"category_scores_gemma":[0.02814232,0.0002458322,0.001033572,0.006780533,0.001967866,0.004118199,0.00213728,0.002753084,0.0005553989],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002107184,"about_ca_system_score_gemma":0.002371561,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02062012,"about_ca_topic_score_gemma":0.009317704,"domain_scores_codex":[0.9966618,0.001426898,0.0001742062,0.0004181916,0.0006941813,0.0006247781],"domain_scores_gemma":[0.9443317,0.03806216,0.01051767,0.001654494,0.00273226,0.002701695],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00008491731,0.0002595666,0.9607806,0.0001040241,0.0001976683,0.0001977413,0.0007040349,0.007722186,0.0001126335,0.01720908,0.002602685,0.01002484],"study_design_scores_gemma":[0.00002845893,0.0001267185,0.9146366,0.000249638,0.0001228297,0.0002316173,0.006489147,0.05477646,0.0002802257,0.01408279,0.008929634,0.00004586138],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9783981,0.002142005,0.00334723,0.004938139,0.00003175581,0.00006152439,0.001012235,0.00002845926,0.01004064],"genre_scores_gemma":[0.9986295,0.0003040118,0.000307782,0.0001377722,0.00002859959,0.00001844673,0.0002959397,0.00001255104,0.0002655636],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02062012,"threshold_uncertainty_score":0.04100019,"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."}}