{"id":"W4327521867","doi":"10.1109/tii.2023.3257308","title":"Blockchain-Empowered Edge Intelligence for TACS Obstacle Detection: System Design and Performance Optimization","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Industrial Informatics","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Fundamental Research Funds for the Central Universities; Natural Science Foundation of Beijing Municipality; National Natural Science Foundation of China","keywords":"Blockchain; Obstacle; Computer science; Enhanced Data Rates for GSM Evolution; Embedded system; Computer security; Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005994593,0.0002204859,0.0003118474,0.000478625,0.0004249538,0.00008438949,0.00009735932,0.000316712,0.00001615677],"category_scores_gemma":[0.00008810069,0.0002193993,0.0000924122,0.0009368161,0.00007534579,0.000205696,0.000002687992,0.0003722865,0.00004536143],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003396624,"about_ca_system_score_gemma":0.0002254218,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001404932,"about_ca_topic_score_gemma":0.000001527601,"domain_scores_codex":[0.9984645,0.00004155307,0.0007096013,0.0001702303,0.0002956825,0.0003184828],"domain_scores_gemma":[0.9984507,0.0007067228,0.0001799645,0.0002972587,0.0001996951,0.0001656711],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005043983,0.00006129358,0.000005845496,0.0003741884,0.00006529435,0.000001718262,0.001136974,0.9490043,0.00006433337,0.00000610806,0.0009331101,0.04784242],"study_design_scores_gemma":[0.001569641,0.0009786282,0.000005932824,0.000433583,0.0001746689,0.00004450317,0.001190453,0.9369389,0.05657334,0.00000254089,0.001884311,0.0002035042],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02964904,0.000006840552,0.9658338,0.0006992725,0.001485322,0.001740084,0.00003632491,0.0005111972,0.00003813368],"genre_scores_gemma":[0.9869077,0.00008462254,0.01150148,0.0005789369,0.000209902,0.0003733802,0.00001396657,0.00004393017,0.0002860641],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9572587,"threshold_uncertainty_score":0.8946845,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09620691615461965,"score_gpt":0.3055996271998389,"score_spread":0.2093927110452192,"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."}}