{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002190343,0.0002706496,0.0002563296,0.0002756823,0.000265416,0.0002114669,0.001896559,0.0001053002,0.00003237079],"category_scores_gemma":[0.000006748732,0.0002829066,0.0001345857,0.001220437,0.0000353114,0.0001239545,0.001589429,0.0002181691,0.000383456],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001874714,"about_ca_system_score_gemma":0.00003296172,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002123364,"about_ca_topic_score_gemma":0.00001142107,"domain_scores_codex":[0.9981259,0.00009211485,0.0001909858,0.0008972904,0.0001663619,0.0005273116],"domain_scores_gemma":[0.998392,0.00006626677,0.0001452365,0.00105008,0.00009513631,0.0002512289],"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":[0.00002822428,0.00005332668,0.01950012,0.00007186677,0.00007293176,0.0001036897,0.0008669589,0.9329678,0.0004503325,0.04409898,0.000371904,0.001413896],"study_design_scores_gemma":[0.0007115178,0.000152021,0.005078857,0.0001120077,0.00003944186,0.00001150335,0.0005907085,0.9791472,0.0003069801,0.003273804,0.009998294,0.0005776606],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8971298,0.0000256737,0.09819517,0.0001209842,0.001752675,0.0002535702,0.000001578275,0.0002880216,0.002232478],"genre_scores_gemma":[0.9949557,0.000002983591,0.002207204,0.0003548078,0.0003618824,1.830546e-7,9.797162e-7,0.00001845895,0.002097781],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09782586,"threshold_uncertainty_score":0.9999623,"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."}}