{"id":"W2790265691","doi":"10.1109/tii.2018.2818932","title":"Fog Computing for Energy-Aware Load Balancing and Scheduling in Smart Factory","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Industrial Informatics","topic":"Digital Transformation in Industry","field":"Engineering","cited_by":323,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"National Natural Science Foundation of China","keywords":"Distributed computing; Computer science; Scheduling (production processes); Load balancing (electrical power); Energy consumption; Particle swarm optimization; Job shop scheduling; Workload; Mathematical optimization; Engineering; Embedded system; Algorithm","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":[0.0002615889,0.0002381894,0.0002514787,0.0002784078,0.0001740576,0.0001429289,0.0001334899,0.0003678039,0.00002454869],"category_scores_gemma":[0.00001184057,0.0002634515,0.00006799867,0.0003253328,0.00008488209,0.0008646011,0.00000171286,0.000499634,0.00001715357],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002617996,"about_ca_system_score_gemma":0.0000951676,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002358707,"about_ca_topic_score_gemma":0.00004572277,"domain_scores_codex":[0.9984983,0.0000126417,0.0007807229,0.0001002962,0.0002313404,0.0003766775],"domain_scores_gemma":[0.9993257,0.0002145239,0.00007404798,0.0001673371,0.0000993545,0.0001190577],"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":[0.0001399885,0.00009734164,0.0004011804,0.0003887444,0.0001748816,0.000001551395,0.00698013,0.7013549,0.0001579214,0.0002903226,0.001358296,0.2886548],"study_design_scores_gemma":[0.002833334,0.0002024893,0.00002983689,0.0004667886,0.00003127185,0.00001456898,0.002684003,0.9636759,0.02367012,0.00008509812,0.005776323,0.0005303014],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1779354,0.000007108436,0.8152133,0.00002174558,0.001660935,0.0002761083,0.00008569717,0.0002571346,0.004542594],"genre_scores_gemma":[0.9977781,0.00001181156,0.001776668,0.00008807312,0.0002211463,0.0000315557,0.000009064385,0.00003403765,0.00004952499],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8198427,"threshold_uncertainty_score":0.9999818,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03717756693143058,"score_gpt":0.2443581678167706,"score_spread":0.20718060088534,"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."}}