{"id":"W4408323613","doi":"10.1109/tsipi.2025.3550155","title":"A Hybrid Deep-Belief and Knowledge-Based Neural Network for Efficient Prediction of Jitter in the Presence of Multiple PDN Noise Sources","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Signal and Power Integrity","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Jitter; Artificial neural network; Noise (video); Computer science; Deep neural networks; Artificial intelligence; Deep belief network; Machine learning; Telecommunications","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":[0.0008399545,0.0001214241,0.0001980976,0.0001373768,0.0001388607,0.00004629136,0.0002586053,0.00005490041,0.000004540502],"category_scores_gemma":[0.00002286585,0.00008507205,0.00008877656,0.0002935897,0.0001479218,0.00009294734,0.000004803931,0.000278208,2.533853e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001116456,"about_ca_system_score_gemma":0.00005498838,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009450084,"about_ca_topic_score_gemma":0.00003480053,"domain_scores_codex":[0.9988789,0.0002920238,0.0002745642,0.0002498985,0.0001385721,0.0001660735],"domain_scores_gemma":[0.9981364,0.001448186,0.00006176216,0.000200219,0.0001220358,0.00003139241],"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":[0.004702938,0.005292467,0.007251685,0.00127526,0.0002879401,0.00002090148,0.02137889,0.5405799,0.0563361,0.00318647,0.002444947,0.3572425],"study_design_scores_gemma":[0.001363845,0.000473477,0.006017856,0.0002068263,0.00003781105,0.000006503287,0.00008400026,0.920428,0.0699432,0.001089704,0.0002414416,0.0001072982],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2428659,0.000262593,0.7560331,0.0002114788,0.0002580125,0.0002539849,0.00002219795,0.00001415186,0.00007854198],"genre_scores_gemma":[0.9933129,0.00000645804,0.006400489,0.0001806547,0.00001594129,0.00003657495,7.199296e-7,0.000003395123,0.00004288679],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.750447,"threshold_uncertainty_score":0.3469138,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02117181554015441,"score_gpt":0.2714253270966247,"score_spread":0.2502535115564703,"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."}}