{"id":"W2883922993","doi":"10.1109/mcom.2018.1700557","title":"Big Sensed Data: Evolution, Challenges, and a Progressive Framework","year":2018,"lang":"en","type":"article","venue":"IEEE Communications Magazine","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Big data; Computer science; Data science; Internet of Things; Cloud computing; Status quo; Analytics; Data management; Computer security; Data mining","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":[0.02976777,0.001491126,0.001549597,0.005001534,0.004037157,0.02153972,0.00651899,0.007033619,0.00222712],"category_scores_gemma":[0.02087738,0.001220183,0.001148683,0.005553985,0.02292404,0.04591844,0.01274995,0.01413977,0.0007844671],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006795375,"about_ca_system_score_gemma":0.0102193,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006151208,"about_ca_topic_score_gemma":0.006112269,"domain_scores_codex":[0.9895034,0.004495643,0.0008880366,0.00140493,0.003167559,0.0005405588],"domain_scores_gemma":[0.9775234,0.012233,0.0009037974,0.002996167,0.004452365,0.00189131],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0000519131,0.00007155097,0.001971856,0.001191919,0.00005589778,0.0001775801,0.001715866,0.004319516,0.0006047229,0.8833716,0.01671555,0.08975213],"study_design_scores_gemma":[0.00001820435,0.00007117136,0.001029933,0.002262707,0.00003785388,0.0002797369,0.005086827,0.02006667,0.0004823107,0.7324023,0.2381647,0.0000976073],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"commentary","genre_gemma":"methods","genre_scores_codex":[0.00970053,0.2031307,0.2234485,0.5237246,0.006067551,0.000421772,0.000715378,0.0006972306,0.03209381],"genre_scores_gemma":[0.2637655,0.2788607,0.3882309,0.04156177,0.01722449,0.00124851,0.001203463,0.0003651298,0.007539527],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.02976777,"threshold_uncertainty_score":0.1574289,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1043819127254549,"score_gpt":0.3251853841879324,"score_spread":0.2208034714624775,"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."}}