{"id":"W2915666855","doi":"10.1109/jproc.2019.2894515","title":"Scalable Personalized IoT Networks","year":2019,"lang":"en","type":"article","venue":"Proceedings of the IEEE","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada; Carleton University","keywords":"Computer science; Scalability; Context (archaeology); Adaptability; Data science; World Wide Web","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000674265,0.0007373499,0.0006409877,0.0005027353,0.0009288122,0.001606208,0.001440311,0.0008029423,0.007493734],"category_scores_gemma":[0.002134364,0.0003399134,0.0003906515,0.0009312095,0.0005012624,0.003505122,0.002275507,0.001436931,0.001741861],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001284455,"about_ca_system_score_gemma":0.0008025159,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001817127,"about_ca_topic_score_gemma":0.003386603,"domain_scores_codex":[0.9994034,0.000106512,0.00002177939,0.000157916,0.0002008424,0.0001095045],"domain_scores_gemma":[0.9992958,0.0002203432,0.00005141118,0.0002223982,0.0001465432,0.00006340217],"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":[0.0003096233,0.000131322,0.001420593,0.0004600007,0.0001309246,0.0006808948,0.0003273194,0.2727887,0.01591324,0.26042,0.102833,0.3445844],"study_design_scores_gemma":[0.00003348729,0.00006809706,0.0007108824,0.00006061924,0.00004475456,0.0003379926,0.0001837299,0.7613343,0.00316043,0.1417985,0.09223021,0.00003695639],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04039051,0.006173344,0.7996639,0.005247475,0.001684436,0.0003822736,0.001281817,0.006537823,0.1386385],"genre_scores_gemma":[0.834664,0.005732807,0.1262676,0.001289666,0.0008738865,0.000407575,0.002100423,0.0003672183,0.02829685],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007493734,"threshold_uncertainty_score":0.02506906,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009532122239417549,"score_gpt":0.2055353346383912,"score_spread":0.1960032123989737,"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."}}