{"id":"W3132578278","doi":"10.3390/fi13020048","title":"Blockchain-Enabled Edge Intelligence for IoT: Background, Emerging Trends and Open Issues","year":2021,"lang":"en","type":"article","venue":"Future Internet","topic":"Blockchain Technology Applications and Security","field":"Computer Science","cited_by":51,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Guangdong Provincial Pearl River Talents Program","keywords":"Blockchain; Computer science; Data science; Enhanced Data Rates for GSM Evolution; Internet of Things; Enabling; Computer security; Cryptocurrency; Edge computing; Open research; Verifiable secret sharing; World Wide Web; Artificial intelligence; Set (abstract data type)","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003010189,0.0001719136,0.0002452851,0.0001198465,0.0001407469,0.0003524008,0.001615207,0.0001682178,0.0001127738],"category_scores_gemma":[0.00002473726,0.0001619613,0.00005989003,0.0005327027,0.00006746668,0.0001010525,0.001296274,0.0002138112,0.00001677281],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002733159,"about_ca_system_score_gemma":0.00004564357,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005866664,"about_ca_topic_score_gemma":0.0002725974,"domain_scores_codex":[0.9986864,0.00004168936,0.0002584306,0.0006310098,0.00009618396,0.0002862948],"domain_scores_gemma":[0.9989045,0.00007628655,0.00008518565,0.0007112948,0.000146933,0.00007576795],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000007872272,0.00007722607,0.00008970384,0.00002042763,0.00004120404,0.00001328101,0.00135659,0.000004212154,0.0002391468,0.7366212,0.01979438,0.2417347],"study_design_scores_gemma":[0.0003669358,0.0001235285,0.0003150879,0.00004474432,0.0000168895,0.0001154866,0.0009789362,0.05360058,0.02230291,0.07402058,0.8477222,0.0003920901],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0362353,0.006368097,0.8767071,0.07455144,0.001403677,0.0005146564,0.00002436251,0.0004540715,0.003741286],"genre_scores_gemma":[0.7394494,0.0001853473,0.2434383,0.001516292,0.0006812369,0.0003108812,0.00002644612,0.00002790799,0.01436422],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8279278,"threshold_uncertainty_score":0.6604589,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02177315274417474,"score_gpt":0.3024786049682087,"score_spread":0.280705452224034,"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."}}