{"id":"W2026222237","doi":"10.1109/iscas.2010.5537244","title":"Stochastic delay differential equation and its application on communications","year":2010,"lang":"en","type":"article","venue":"","topic":"Neural Networks Stability and Synchronization","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Additive white Gaussian noise; Robustness (evolution); Computer science; Bit error rate; Gaussian; Binary number; Modulation (music); Transmission (telecommunications); Algorithm; Mathematics; White noise; Channel (broadcasting); Control theory (sociology); Telecommunications; 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":[0.00009919357,0.00006333811,0.00005451543,0.00003694396,0.0001899257,0.00007500604,0.0004142188,0.0000479442,0.00001541393],"category_scores_gemma":[0.00003689885,0.00005581126,0.0000131673,0.0001444811,0.00003162661,0.0002542252,0.0001572847,0.0001475616,0.0000343852],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000100723,"about_ca_system_score_gemma":0.0000126564,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007017857,"about_ca_topic_score_gemma":0.00009598937,"domain_scores_codex":[0.9994453,0.00003810014,0.0001188407,0.0001943976,0.0001114256,0.00009189365],"domain_scores_gemma":[0.9990926,0.0001514072,0.00004319349,0.000604053,0.00006159833,0.00004713027],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000002468621,0.00006848753,0.00005204824,0.000003135469,0.000002263454,5.020357e-8,0.0001199704,0.0005780762,0.00917943,0.937244,0.00002046057,0.05272961],"study_design_scores_gemma":[0.0001061177,0.00003108228,0.001514559,0.000002156939,0.000002425466,0.000002360691,0.000002139905,0.9909597,0.0005378762,0.006643819,0.0001299239,0.00006781156],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09704097,0.0000112177,0.900368,0.001682026,0.0001254206,0.000212103,7.248834e-7,0.0001006428,0.000458927],"genre_scores_gemma":[0.9958079,0.000005753878,0.003926356,0.0001250222,0.00004215828,0.00003150171,0.00001335694,0.000003313694,0.00004456369],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9903817,"threshold_uncertainty_score":0.2275917,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02549493819190108,"score_gpt":0.2589303588245058,"score_spread":0.2334354206326047,"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."}}