{"id":"W2894021503","doi":"10.1145/3267809.3267812","title":"Dynamic and Decentralized Global Analytics via Machine Learning","year":2018,"lang":"en","type":"article","venue":"","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Huawei Technologies","keywords":"Computer science; Analytics; Distributed computing; Resource (disambiguation); Data analysis; Big data; Volume (thermodynamics); Software; Cloud computing; Data science; Data mining; Operating system; Computer network","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001674936,0.00009135487,0.00009550955,0.00003225917,0.000168786,0.0001289908,0.0003337829,0.0000245246,0.00002235384],"category_scores_gemma":[0.00001458145,0.00007298338,0.00002868657,0.0002829939,0.0000567983,0.00001418624,0.0004923884,0.00006224791,0.00005574268],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003566925,"about_ca_system_score_gemma":0.0000073519,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008669853,"about_ca_topic_score_gemma":0.00006738009,"domain_scores_codex":[0.9992064,0.00003922763,0.0001164575,0.000263441,0.0001463619,0.0002281571],"domain_scores_gemma":[0.9995986,0.00002060943,0.00003833823,0.0002297093,0.00003135079,0.00008144286],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002205454,0.0001442587,0.04632491,0.00003562662,0.0001710766,0.00005353335,0.0008413122,0.006111417,0.0001916995,0.06437811,0.001241308,0.8804847],"study_design_scores_gemma":[0.0002569517,0.00007558197,0.005777989,0.000005626903,0.000006610024,0.00001734836,0.000009913214,0.982742,0.00001761237,0.001309666,0.009679644,0.0001011157],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2132641,0.0001393986,0.7781761,0.0009271428,0.0001486699,0.00005015591,1.912468e-7,0.0002630835,0.007031187],"genre_scores_gemma":[0.9631323,0.000008634901,0.03530205,0.0003159028,0.0000220805,3.98398e-7,5.139032e-7,0.000003202585,0.001214906],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9766305,"threshold_uncertainty_score":0.2976176,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007483109413859588,"score_gpt":0.2413636985619899,"score_spread":0.2338805891481303,"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."}}