{"id":"W2973341182","doi":"10.3390/e21090902","title":"Optimization of Big Data Scheduling in Social Networks","year":2019,"lang":"en","type":"article","venue":"Entropy","topic":"AI and Multimedia in Education","field":"Computer Science","cited_by":230,"is_retracted":false,"has_abstract":true,"ca_institutions":"Brandon University","funders":"Natural Science Foundation of Inner Mongolia","keywords":"Computer science; Big data; Scheduling (production processes); Fair-share scheduling; Dynamic priority scheduling; Entropy (arrow of time); Two-level scheduling; Rate-monotonic scheduling; Distributed computing; Mathematical optimization; Data mining; Quality of service; Computer network; Mathematics","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":[],"consensus_categories":[],"category_scores_codex":[0.000137387,0.00003578795,0.00006394071,0.0000447105,0.00001795559,0.00001755253,0.0004666129,0.00003315147,0.00001577512],"category_scores_gemma":[0.00002749669,0.0000357729,0.000009600491,0.0001891459,0.00000905651,0.0002292341,0.0001514207,0.00005485621,0.00001382607],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001717177,"about_ca_system_score_gemma":0.00004332588,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000172601,"about_ca_topic_score_gemma":0.000001576248,"domain_scores_codex":[0.9995359,0.00002427358,0.000109842,0.0001500319,0.00008514758,0.00009484065],"domain_scores_gemma":[0.9995679,0.00003360468,0.00005984275,0.0003020372,0.00002235636,0.0000142643],"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":[0.000007350171,0.0001221095,0.02279185,0.00001276874,0.000008036379,5.862058e-7,0.001665643,0.8919675,0.0003514704,0.02275741,0.0007705612,0.05954474],"study_design_scores_gemma":[0.0001835231,0.000007794106,0.002800301,0.000007248761,0.000001105516,1.843763e-7,0.00002979338,0.9964584,0.00006837455,0.0001140429,0.0002905841,0.00003865264],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03761645,0.00005547205,0.9602251,0.0003239475,0.001390489,0.00008626556,7.590332e-7,0.00001749149,0.0002840296],"genre_scores_gemma":[0.8885468,0.00001516439,0.1109859,0.00004620718,0.0003409792,0.000001822042,0.0000282384,0.000002988884,0.00003185759],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8509304,"threshold_uncertainty_score":0.1458777,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03703419125600126,"score_gpt":0.2813361211476854,"score_spread":0.2443019298916841,"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."}}