{"id":"W2768918001","doi":"10.1109/ficloud.2017.27","title":"SAVI-IoT: A Self-Managing Containerized IoT Platform","year":2017,"lang":"en","type":"article","venue":"","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Cloud computing; Distributed computing; Internet of Things; Resilience (materials science); Edge computing; Service (business); Enhanced Data Rates for GSM Evolution; Architecture; Quality of service; Computer network; Computer security; Operating system; Telecommunications","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":[0.0003901788,0.0001672979,0.0002108743,0.00008089343,0.0009256786,0.0009656269,0.00185545,0.00005934332,0.000008483257],"category_scores_gemma":[0.00005175322,0.000142312,0.0000875789,0.00008381828,0.00003678413,0.0004105101,0.0009863694,0.0001528255,0.0002042636],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000521334,"about_ca_system_score_gemma":0.00006162973,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001738814,"about_ca_topic_score_gemma":0.000009886327,"domain_scores_codex":[0.9986874,0.00001346021,0.0002167955,0.0003845252,0.0002215629,0.0004762647],"domain_scores_gemma":[0.9984542,0.00007071836,0.0001520526,0.001138301,0.00006156908,0.000123138],"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":[0.00004446533,0.0002698849,0.01203835,0.0001259021,0.0002360091,0.0004270995,0.007903218,0.00005705715,0.002835967,0.2018787,0.05556136,0.718622],"study_design_scores_gemma":[0.002579422,0.0001476017,0.01688094,0.00009391159,0.00001977488,0.00007872777,0.00005729003,0.8123848,0.002655779,0.01602215,0.1481443,0.0009352815],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1961639,0.00008334079,0.4869609,0.005116225,0.0157938,0.0003445018,1.16507e-7,0.001755598,0.2937817],"genre_scores_gemma":[0.882109,0.00000439621,0.1121579,0.0009286638,0.001948517,0.000006106166,7.40184e-7,0.0000176862,0.00282698],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8123278,"threshold_uncertainty_score":0.9311562,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02200612726038394,"score_gpt":0.2580852621318696,"score_spread":0.2360791348714856,"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."}}