{"id":"W2896348584","doi":"10.1109/iccmc.2018.8487843","title":"IoT Hybrid Computing Model for Intelligent Transportation System (ITS)","year":2018,"lang":"en","type":"article","venue":"","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"Horizon College and Seminary","funders":"","keywords":"Cloud computing; Computer science; Intelligent transportation system; Edge computing; SAFER; Internet of Things; Enhanced Data Rates for GSM Evolution; Global Positioning System; Distributed computing; Fog computing; Low latency (capital markets); Smart objects; Wireless; Latency (audio); Computer network; Computer security; Telecommunications; Transport engineering; 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":[0.0002016409,0.0005516804,0.0003073496,0.0003825267,0.000638869,0.001271527,0.00125515,0.00092788,0.003526294],"category_scores_gemma":[0.000261349,0.00015958,0.0006438736,0.0005932833,0.0005040666,0.001717086,0.0007108475,0.000720804,0.0007026912],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008444424,"about_ca_system_score_gemma":0.0007276249,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004628226,"about_ca_topic_score_gemma":0.005002101,"domain_scores_codex":[0.9997581,0.00005066333,0.00001510437,0.0000620236,0.00007743158,0.00003668435],"domain_scores_gemma":[0.9999053,0.00002100918,0.000009266039,0.00001284204,0.0000377191,0.00001386058],"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":[0.0001255788,0.0001056011,0.001445205,0.0002197549,0.00006315381,0.0007065877,0.0002646359,0.5021405,0.008051493,0.4312549,0.0112505,0.04437213],"study_design_scores_gemma":[0.00001574069,0.00005997654,0.0002676013,0.00002617361,0.00002389382,0.0001877026,0.00007728466,0.9308541,0.0008993475,0.04595898,0.0216133,0.00001589199],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03067803,0.001632576,0.8719004,0.002314469,0.0005666615,0.0002290187,0.0004714336,0.0006786628,0.09152866],"genre_scores_gemma":[0.8278353,0.00235973,0.1287526,0.000531052,0.0002299557,0.0005751611,0.0004649129,0.0000975017,0.03915385],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004628226,"threshold_uncertainty_score":0.01179665,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04033439571491328,"score_gpt":0.2684064661503361,"score_spread":0.2280720704354229,"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."}}