{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001916563,0.001093454,0.001685137,0.0009783193,0.0009730245,0.002136343,0.001918511,0.0009013127,0.002635426],"category_scores_gemma":[0.00544375,0.0007076235,0.0006171621,0.001478027,0.001750216,0.003918998,0.003449805,0.002405787,0.001168071],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001187173,"about_ca_system_score_gemma":0.002197807,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00394814,"about_ca_topic_score_gemma":0.004612776,"domain_scores_codex":[0.9984079,0.0003642229,0.00006619039,0.0005452015,0.0003606476,0.0002558467],"domain_scores_gemma":[0.9972704,0.001152914,0.0002184417,0.0007966947,0.0004083397,0.0001531962],"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":[0.0003633027,0.0002285074,0.002414735,0.0001634362,0.0001299893,0.0001280862,0.000148456,0.6892754,0.00507534,0.04276884,0.01037977,0.2489241],"study_design_scores_gemma":[0.00001244828,0.00001851855,0.0001614852,0.000005721175,0.000006107823,0.00001270658,0.00001602992,0.9717381,0.0005193388,0.02659981,0.0009033654,0.000006297134],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01586947,0.000589978,0.9759911,0.0005982068,0.00008529354,0.000068945,0.0001490472,0.002626992,0.004020995],"genre_scores_gemma":[0.800979,0.0007012424,0.1919399,0.0003254407,0.0002693925,0.0002117273,0.0007761243,0.0003359314,0.004461315],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00394814,"threshold_uncertainty_score":0.01013583,"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."}}