{"id":"W4392979711","doi":"10.1109/jiot.2024.3378202","title":"Sparse Bayesian Tensor Completion for Data Recovery in Intelligent IoT Systems","year":2024,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Tensor decomposition and applications","field":"Mathematics","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"St. Francis Xavier University","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Computer science; Robustness (evolution); Missing data; Bayesian probability; Artificial intelligence; Bayesian inference; Big data; Data mining; Machine learning; Data modeling; Domain knowledge; Database","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":[0.0009074314,0.0001218085,0.0002662433,0.0002431402,0.00003429496,0.0002058102,0.0005216079,0.00007013982,0.00008572525],"category_scores_gemma":[0.0001330201,0.0001052424,0.000113717,0.0001118605,0.00003191215,0.0001728201,0.00005440065,0.0003142175,0.00002750671],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000116987,"about_ca_system_score_gemma":0.00003784992,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006781707,"about_ca_topic_score_gemma":0.00001203881,"domain_scores_codex":[0.9986146,0.0000563106,0.0007328858,0.0002191994,0.0002088202,0.0001681548],"domain_scores_gemma":[0.9988596,0.0003964877,0.0002372635,0.0003215965,0.0001139654,0.00007103143],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005183941,0.001292916,0.0004910764,0.002820852,0.000871046,0.0001562641,0.007216637,0.001487186,0.03048653,0.1794254,0.7405479,0.03468575],"study_design_scores_gemma":[0.0006234967,0.0002581739,0.00007133772,0.003633063,0.0001506204,0.001428381,0.0006136034,0.8188237,0.005408866,0.1123979,0.05622732,0.0003635225],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1459402,0.0004267442,0.8490444,0.001380485,0.001938084,0.0004898247,0.00009653506,0.00007997382,0.0006037556],"genre_scores_gemma":[0.9650646,0.00004499906,0.0331189,0.0001287153,0.0003309978,0.00002236517,0.0000246118,0.0000370717,0.001227767],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8191243,"threshold_uncertainty_score":0.4291659,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1406877771943433,"score_gpt":0.3738669681349511,"score_spread":0.2331791909406078,"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."}}