{"id":"W1989121134","doi":"10.1145/2492517.2500279","title":"Efficient mobile services consumption in mHealth","year":2013,"lang":"en","type":"article","venue":"","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Computer science; mHealth; Mobile computing; Mobile device; Data access; Middleware (distributed applications); Synchronization (alternating current); Low latency (capital markets); Data synchronization; Cloud computing; Computer security; Computer network; Health care; World Wide Web; Wireless sensor network; Database; Operating system","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0006185542,0.0003435546,0.0004571826,0.0004253448,0.0004597448,0.001189093,0.0004986518,0.0005134886,0.001604722],"category_scores_gemma":[0.00123451,0.0001795912,0.0002014969,0.0006705783,0.0003127944,0.0008894899,0.0006116017,0.0002215695,0.0003224712],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009521428,"about_ca_system_score_gemma":0.0005584927,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005210678,"about_ca_topic_score_gemma":0.004506985,"domain_scores_codex":[0.9995419,0.0001327755,0.00002560754,0.00005981996,0.0001592972,0.00008060025],"domain_scores_gemma":[0.9997286,0.0001160528,0.00002952713,0.0000380681,0.00006587057,0.00002177727],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001947644,0.0005482383,0.01324204,0.0003687706,0.0001425111,0.001122167,0.0006092269,0.3174574,0.07878161,0.05892755,0.008126132,0.5187267],"study_design_scores_gemma":[0.00003737088,0.0001815613,0.00549735,0.00002679182,0.00005403586,0.000220495,0.0003116573,0.9555475,0.01761158,0.01270335,0.007785833,0.00002250506],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4697358,0.004273554,0.4980228,0.001235292,0.000128741,0.0002826079,0.0003790417,0.001188588,0.02475358],"genre_scores_gemma":[0.9729868,0.0003829167,0.02400399,0.00004855026,0.00001932652,0.00002224241,0.00007562726,0.000037597,0.002422981],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005210678,"threshold_uncertainty_score":0.01036066,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01476410834314543,"score_gpt":0.2617751279623752,"score_spread":0.2470110196192298,"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."}}