{"id":"W2963839773","doi":"10.48550/arxiv.1907.10890","title":"DeFog: Fog Computing Benchmarks","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Benchmarking; Cloud computing; Computer science; Suite; Enhanced Data Rates for GSM Evolution; Software deployment; Edge computing; Distributed computing; Set (abstract data type); Performance improvement; Edge device; Computation; Operating system; Telecommunications; Engineering","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":[0.002531295,0.001571153,0.0007113506,0.00175861,0.0009302886,0.001939634,0.00251262,0.00102899,0.0024852],"category_scores_gemma":[0.007782951,0.0003674193,0.0006144029,0.002747592,0.0006526292,0.001728278,0.001571395,0.001729558,0.001004895],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001546936,"about_ca_system_score_gemma":0.001617382,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007772806,"about_ca_topic_score_gemma":0.008231647,"domain_scores_codex":[0.9969009,0.00068951,0.000280339,0.0003215614,0.001321787,0.0004858958],"domain_scores_gemma":[0.9961714,0.001206042,0.000226534,0.0009282065,0.001186136,0.0002816114],"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":[0.001329231,0.001387546,0.01531385,0.002462718,0.0003275967,0.0006129685,0.0004803159,0.3885325,0.01466602,0.06394533,0.311468,0.199474],"study_design_scores_gemma":[0.0002150124,0.0007264895,0.0105443,0.0002574788,0.00007516993,0.0004551422,0.0003932089,0.7729471,0.02990598,0.03606386,0.1482804,0.0001358963],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.3667058,0.007360997,0.3304093,0.003194493,0.002517384,0.003344988,0.04672857,0.06249543,0.1772429],"genre_scores_gemma":[0.7946415,0.001922016,0.1455115,0.0008795827,0.0001475104,0.001268925,0.04451781,0.004057698,0.0070536],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007772806,"threshold_uncertainty_score":0.01545513,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0605566664140297,"score_gpt":0.1778887225432011,"score_spread":0.1173320561291714,"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."}}