{"id":"W4400187859","doi":"10.1109/ticps.2024.3420823","title":"Deep Learning Aided Minimum Mean Square Error Estimation of Gaussian Source in Industrial Internet-of-Things Networks","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Industrial Cyber-Physical Systems","topic":"Advanced Algorithms and Applications","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"Intel Corporation; National Science Foundation","keywords":"Mean squared error; Minimum mean square error; Gaussian; Estimation; Statistics; Computer science; The Internet; Mean square; Artificial intelligence; Internet of Things; Mathematics; Machine learning; Engineering; World Wide Web; Chemistry","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000196778,0.0002817832,0.0005170942,0.0002719029,0.00006475604,0.00006275348,0.0001803883,0.0003832835,0.00001635464],"category_scores_gemma":[0.00001460204,0.0002795598,0.0001996836,0.0009165559,0.00007485314,0.0003022518,0.000002851658,0.001323643,0.00001576565],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000196079,"about_ca_system_score_gemma":0.00003566686,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005455892,"about_ca_topic_score_gemma":0.00002090211,"domain_scores_codex":[0.998212,0.0001027966,0.0007479572,0.0003251039,0.0003004835,0.0003117049],"domain_scores_gemma":[0.9991325,0.0003783288,0.0001208304,0.000217371,0.00003780469,0.0001131946],"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":[0.00004087175,0.00008139347,0.000004697207,0.00005961674,0.00006804046,0.000002029483,0.0009339594,0.8343247,0.0005111661,0.0001923016,0.00003801917,0.1637432],"study_design_scores_gemma":[0.0007845213,0.0001664655,0.000005811836,0.0008188765,0.00006707482,0.00000394953,0.0004331647,0.9917765,0.005067517,0.00004711664,0.000584123,0.0002448913],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1344772,0.00006953581,0.8627998,0.00002589999,0.001573192,0.0005752926,0.00001102862,0.000271789,0.0001962908],"genre_scores_gemma":[0.9987599,0.000006477875,0.0002108269,0.000001951896,0.0005755532,0.0001709614,0.0000175185,0.00006886694,0.0001879621],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8642827,"threshold_uncertainty_score":0.9999657,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02358364206163877,"score_gpt":0.2524736445670447,"score_spread":0.228890002505406,"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."}}