{"id":"W4384161764","doi":"10.1109/jiot.2023.3294887","title":"Task Class Partitioning for Mobile Computation Offloading","year":2023,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Partition (number theory); Mobile edge computing; Computation offloading; Computation; Distributed computing; Task (project management); Class (philosophy); Heuristic; Task analysis; Parallel computing; Enhanced Data Rates for GSM Evolution; Server; Edge computing; Algorithm; Computer network; Artificial intelligence; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001003209,0.0001117391,0.0001866638,0.0002601446,0.0001735951,0.000329281,0.0006007766,0.00005497134,0.000002118853],"category_scores_gemma":[0.00007424282,0.0001082988,0.0001550022,0.0003018815,0.0000289199,0.0007910505,0.0001202356,0.0002416214,0.00003993596],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006550079,"about_ca_system_score_gemma":0.00004993038,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001065299,"about_ca_topic_score_gemma":2.283095e-7,"domain_scores_codex":[0.9987039,0.00004555865,0.0004555293,0.0001973167,0.0002719172,0.0003257575],"domain_scores_gemma":[0.9989743,0.0002444579,0.0003633181,0.0001130515,0.0002207127,0.00008412445],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009156571,0.0001854468,0.002426303,0.000308484,0.000353575,0.0001307255,0.04738741,0.04868979,0.05762157,0.003040636,0.5050746,0.3346899],"study_design_scores_gemma":[0.0004086934,0.0002238373,0.0002215096,0.0002713699,0.00000997419,0.0001520235,0.00007043804,0.9663126,0.01464479,0.008433591,0.009094006,0.0001571575],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2647238,0.00003782023,0.7237,0.0002342599,0.01080367,0.00009084344,1.672887e-7,0.0001290969,0.0002802859],"genre_scores_gemma":[0.9683815,0.000006794982,0.02958382,0.0002013305,0.001472999,0.000008662094,0.000002723704,0.00001528027,0.0003268888],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9176228,"threshold_uncertainty_score":0.4416298,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02711831608418536,"score_gpt":0.2920052887515052,"score_spread":0.2648869726673198,"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."}}