{"id":"W3022421643","doi":"","title":"Dogfooding: using IBM cloud services to monitor IBM cloud infrastructure.","year":2019,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"IBM; Cloud computing; Computer science; Operating system","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":[0.0008518306,0.0007573796,0.0003958236,0.001111795,0.0004399887,0.0009288981,0.0008549906,0.0003979871,0.003865515],"category_scores_gemma":[0.002943039,0.0002256828,0.0001520839,0.001385718,0.0003202,0.001585532,0.0008962187,0.0005992585,0.001615454],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005378887,"about_ca_system_score_gemma":0.0007041693,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01527845,"about_ca_topic_score_gemma":0.02024868,"domain_scores_codex":[0.9993644,0.0001447489,0.00003146397,0.0001401033,0.0002346463,0.00008460837],"domain_scores_gemma":[0.9986835,0.0002314655,0.0001550621,0.000295726,0.0003807296,0.0002535072],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002565609,0.0005970969,0.2157678,0.000312488,0.0003553032,0.001846011,0.001482475,0.02041623,0.05933074,0.005676993,0.2389484,0.4527007],"study_design_scores_gemma":[0.0003436517,0.0008021951,0.1146873,0.00005956134,0.0001645955,0.0008468021,0.001745072,0.7262388,0.0579963,0.01434903,0.08257758,0.0001890907],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6204048,0.001525013,0.09922097,0.002222372,0.0006043918,0.0004869898,0.01285209,0.2167858,0.04589764],"genre_scores_gemma":[0.9516186,0.0002383542,0.03546311,0.0002419881,0.00005807581,0.00006907561,0.004320193,0.001848969,0.006141644],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01527845,"threshold_uncertainty_score":0.03037906,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02659021964427626,"score_gpt":0.179192873345482,"score_spread":0.1526026537012057,"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."}}