{"id":"W3024193050","doi":"10.1109/smartcloud.2019.00020","title":"oHealth: Opportunistic Healthcare in Public Transit through Fog and Edge Computing","year":2019,"lang":"en","type":"article","venue":"","topic":"Human Mobility and Location-Based Analysis","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Public healthcare; Health care; Enhanced Data Rates for GSM Evolution; Fog computing; Edge computing; Transit (satellite); Public transport; Computer security; Transport engineering; Internet of Things; Telecommunications; Engineering; Political science","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":[0.0002937524,0.0003840218,0.0003616864,0.000270309,0.0005529438,0.0009266531,0.0009587566,0.0005304595,0.001519225],"category_scores_gemma":[0.000508961,0.000124738,0.0002587112,0.0003239994,0.0003604273,0.001026408,0.001632826,0.000387523,0.0004428876],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004268383,"about_ca_system_score_gemma":0.0008481191,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00419848,"about_ca_topic_score_gemma":0.005263768,"domain_scores_codex":[0.9997235,0.00004965626,0.00001773105,0.00005295089,0.00005598695,0.0001001636],"domain_scores_gemma":[0.9997815,0.00004103536,0.00002491699,0.00004216442,0.00004425077,0.00006612929],"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":[0.003847983,0.0009554708,0.03694846,0.001061649,0.0003426582,0.005266876,0.001905447,0.02817096,0.07533979,0.03717854,0.196862,0.6121202],"study_design_scores_gemma":[0.000460745,0.00140844,0.02362951,0.000211718,0.0003080163,0.003705957,0.00189873,0.6544859,0.02809622,0.04030539,0.2452636,0.0002257484],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3639588,0.00661142,0.5421054,0.007922521,0.002290318,0.001540824,0.00203112,0.02628654,0.04725294],"genre_scores_gemma":[0.9673006,0.0006348435,0.02552906,0.001266143,0.0001391741,0.0001103205,0.0006462839,0.0001003884,0.004273243],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00419848,"threshold_uncertainty_score":0.008348107,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08396664192509465,"score_gpt":0.3532788577623486,"score_spread":0.269312215837254,"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."}}