{"id":"W3158281120","doi":"10.3390/iot2020014","title":"A Greedy Scheduling Approach for Peripheral Mobile Intelligent Systems","year":2021,"lang":"en","type":"article","venue":"IoT","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Chicoutimi","funders":"","keywords":"Computer science; Distributed computing; Cloudlet; Load balancing (electrical power); Scheduling (production processes); Wireless network; Greedy algorithm; Heuristics; Mobile computing; Wireless; Mobile device; Computer network; Algorithm; Operating system","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":[0.0002659944,0.0001143328,0.0001675407,0.0000393296,0.0001655439,0.0003144946,0.0004385041,0.00005614576,0.000001598997],"category_scores_gemma":[0.00003021001,0.0001099295,0.00009574802,0.0002472932,0.00001564714,0.00008908004,0.0002346045,0.0001043462,0.00001453688],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006049167,"about_ca_system_score_gemma":0.0000977931,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001794698,"about_ca_topic_score_gemma":3.584561e-7,"domain_scores_codex":[0.9988929,0.00004021499,0.0002067357,0.0003805015,0.0001550278,0.0003246027],"domain_scores_gemma":[0.999309,0.00005941369,0.00005145629,0.0003699892,0.0001338206,0.00007630205],"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":[0.00004689092,0.001308889,0.003026217,0.001750424,0.000389624,0.0002037268,0.02346923,0.3207005,0.01185702,0.06727739,0.02265384,0.5473163],"study_design_scores_gemma":[0.0001569468,0.00005162429,0.00002714257,0.00003097128,0.000004278274,0.00005026893,0.0001841239,0.9659681,0.001940493,0.0001578084,0.03126267,0.0001656218],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03062383,0.001404743,0.9587334,0.00005286812,0.007174779,0.0002501315,2.513971e-7,0.0001437613,0.001616177],"genre_scores_gemma":[0.5504459,0.000005635762,0.4433728,0.0002007211,0.004286194,0.0001425195,0.00001256291,0.00002439943,0.001509262],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6452675,"threshold_uncertainty_score":0.4482796,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03511785922869599,"score_gpt":0.2679766479064904,"score_spread":0.2328587886777944,"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."}}