{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004765185,0.0004814219,0.0005238145,0.0003620946,0.0003076131,0.0003159171,0.003338039,0.0004412366,0.00001565014],"category_scores_gemma":[0.00003677312,0.0005757936,0.000368087,0.0006883696,0.00008374319,0.0003827754,0.006703413,0.001094953,0.0003433826],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002839656,"about_ca_system_score_gemma":0.0003288662,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001429285,"about_ca_topic_score_gemma":0.000004195393,"domain_scores_codex":[0.9969544,0.0001742697,0.0003190982,0.001653838,0.0001513588,0.000747069],"domain_scores_gemma":[0.997153,0.0002412928,0.0003924382,0.001801416,0.0001974682,0.0002144161],"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":[0.0000423699,0.0003132496,0.04173866,0.0006390779,0.0004416106,0.001303749,0.002439186,0.7874848,0.00005421233,0.115828,0.03399933,0.01571575],"study_design_scores_gemma":[0.0004002534,0.00004387884,0.002815689,0.0002087972,0.00004301625,0.00001227828,0.00002943916,0.9749569,0.00005734265,0.01680474,0.003877262,0.0007503666],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2837923,0.00008969081,0.6798562,0.0000867831,0.01464281,0.0002863657,9.037456e-7,0.0003747797,0.02087009],"genre_scores_gemma":[0.9927574,0.00002712424,0.004616484,0.0001810671,0.00122416,1.746551e-7,0.00002966922,0.0000268315,0.001137071],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7089651,"threshold_uncertainty_score":0.9996694,"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."}}