{"id":"W4287725505","doi":"10.48550/arxiv.2007.04573","title":"Completion Time Minimization in Fog-RANs using D2D Communications and\\n Rate-Aware Network Coding","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Cooperative Communication and Network Coding","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia, Okanagan Campus","funders":"","keywords":"Computer science; Linear network coding; Scheduling (production processes); Exploit; Telecommunications link; Mathematical optimization; Minification; Radio access network; Computer network; Mathematics; Base station","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000947508,0.001191679,0.0009687227,0.0004924827,0.0006972785,0.001129668,0.001569288,0.0008941448,0.002168604],"category_scores_gemma":[0.002594774,0.0004963914,0.000648916,0.001022779,0.0008892679,0.001210533,0.001206972,0.001075458,0.0002011102],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002417767,"about_ca_system_score_gemma":0.002426718,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01695913,"about_ca_topic_score_gemma":0.02011131,"domain_scores_codex":[0.9993981,0.0001401262,0.00001821546,0.0001558178,0.0001163754,0.0001712721],"domain_scores_gemma":[0.9984338,0.0009852775,0.0001629768,0.0001051383,0.0001541119,0.0001587085],"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":[0.00004976676,0.00003227954,0.0002349658,0.00003560984,0.000008973969,0.00004945436,0.00002766893,0.9849293,0.0007002073,0.006400852,0.0007533453,0.006777577],"study_design_scores_gemma":[0.000005607485,0.00001403219,0.00005648666,0.000002717407,0.000002483184,0.000008568541,0.00001276761,0.9970173,0.0002565833,0.002416503,0.0002036766,0.000003307618],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07895993,0.0004703987,0.9121472,0.0004294424,0.00008469047,0.0001343502,0.0002714438,0.0003354835,0.007167202],"genre_scores_gemma":[0.8563554,0.0003716238,0.1392371,0.0001290804,0.00003692662,0.0001422653,0.0002987503,0.0001120187,0.003316788],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01695913,"threshold_uncertainty_score":0.03372085,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.200348078231877,"score_gpt":0.2368972927178532,"score_spread":0.03654921448597626,"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."}}