{"id":"W2773756654","doi":"10.1109/fwc.2017.8368538","title":"Implementing an edge-fog-cloud architecture for stream data management","year":2017,"lang":"en","type":"preprint","venue":"","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"Natural Sciences and Engineering Research Council of Canada; Compute Canada; Cisco Systems","keywords":"Computer science; Cloud computing; Data stream mining; Process (computing); The Internet; Analytics; Big data; Data stream; Architecture; Edge device; Distributed computing; Real-time computing; Database; Telecommunications; Data mining; World Wide Web; Operating system","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":["metaepi_narrow","scholarly_communication","open_science"],"consensus_categories":["open_science"],"category_scores_codex":[0.001776217,0.0004680212,0.000426979,0.0001965629,0.00091346,0.002064895,0.01171325,0.0001850698,0.000007834394],"category_scores_gemma":[0.00004644165,0.0004277063,0.0001516844,0.0000709474,0.00004047862,0.0004265006,0.03544002,0.000487101,0.0000207273],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005409516,"about_ca_system_score_gemma":0.0001322964,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001604973,"about_ca_topic_score_gemma":0.00007653085,"domain_scores_codex":[0.9960473,0.00007247891,0.0004963898,0.001930232,0.0003841299,0.001069467],"domain_scores_gemma":[0.991828,0.00009828978,0.0004529498,0.00733766,0.0001069107,0.0001762175],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000005112882,0.00007834192,0.0001219099,0.0004357477,0.000164708,0.0000198122,0.0004909661,0.0001585335,0.000007972151,0.00694176,0.08759101,0.9039841],"study_design_scores_gemma":[0.0005782234,0.00007671573,0.0004785732,0.0002506016,0.00008835698,0.00001090295,0.0000500462,0.2888528,0.00018063,0.04713829,0.6613058,0.0009891191],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001414701,0.00009978467,0.9543097,0.0009764044,0.02096316,0.001128166,0.00002226125,0.000485154,0.02060068],"genre_scores_gemma":[0.01100613,0.00002598428,0.9676384,0.0004095021,0.01610129,0.00009704655,0.001270371,0.00007192347,0.003379298],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.902995,"threshold_uncertainty_score":0.9998175,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0833820954226462,"score_gpt":0.3478676563971457,"score_spread":0.2644855609744995,"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."}}