{"id":"W4281686225","doi":"10.1155/2022/4390923","title":"Automatic Scaling Mechanism of Intermodal EDI System under Green Cloud Computing","year":2022,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Key Research and Development Program of China; Sichuan Agricultural University; China Railway","keywords":"Computer science; Cloud computing; Distributed computing; Energy consumption; Scalability; Resource allocation; Scheduling (production processes); Database; Computer network; Operating system; Engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005949878,0.0005299084,0.0004887925,0.0005327427,0.001000885,0.001180585,0.00121937,0.0003559031,0.00181493],"category_scores_gemma":[0.00118666,0.0001732034,0.0003545337,0.0006204173,0.0004770028,0.001405874,0.001051799,0.0005376711,0.00032834],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001249127,"about_ca_system_score_gemma":0.0009805523,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004764742,"about_ca_topic_score_gemma":0.002950857,"domain_scores_codex":[0.9993331,0.00007183805,0.00003468991,0.000184841,0.0002140385,0.0001615042],"domain_scores_gemma":[0.9994981,0.00005937726,0.00005625968,0.0001262376,0.0001925233,0.0000674281],"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":[0.0006861616,0.0004086411,0.01759174,0.0002168777,0.0001264566,0.0008461454,0.0007291986,0.5220013,0.118557,0.03911417,0.01018145,0.2895408],"study_design_scores_gemma":[0.00002281564,0.00006199587,0.001719928,0.000006964998,0.00001670035,0.00007444309,0.00009644693,0.9800174,0.01118142,0.003698167,0.003082367,0.00002125306],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3164241,0.0006477556,0.6551853,0.000580775,0.000236724,0.0003078763,0.0002147371,0.004326386,0.02207625],"genre_scores_gemma":[0.9765099,0.0001012038,0.02164337,0.00005780963,0.00001745552,0.0000502667,0.0000846139,0.00003801182,0.001497395],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004764742,"threshold_uncertainty_score":0.009473979,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01262727140677701,"score_gpt":0.2732478132156925,"score_spread":0.2606205418089155,"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."}}