{"id":"W4283834071","doi":"10.1364/ao.462827","title":"Underwater wireless optical communication utilizing low-complexity sparse pruned-term-based nonlinear decision-feedback equalization","year":2022,"lang":"en","type":"article","venue":"Applied Optics","topic":"Optical Wireless Communication Technologies","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Science Foundation of Zhejiang Province; National Natural Science Foundation of China","keywords":"Nonlinear system; Computer science; Robustness (evolution); Volterra series; Equalization (audio); Control theory (sociology); Wireless; Term (time); Algorithm; Telecommunications; Artificial intelligence; Physics; Decoding methods","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":[0.0004139429,0.0003158899,0.0003592203,0.0002057499,0.0005249155,0.00012434,0.001347771,0.0001923898,0.0001627428],"category_scores_gemma":[0.0000564481,0.0003566902,0.00008726695,0.0006304522,0.000345517,0.0001268498,0.0008870723,0.0008310846,0.0001188034],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003371795,"about_ca_system_score_gemma":0.00004907231,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003600688,"about_ca_topic_score_gemma":0.00001141627,"domain_scores_codex":[0.9979327,0.00007158001,0.000665583,0.0003335555,0.0005339399,0.0004625926],"domain_scores_gemma":[0.9973529,0.0005489186,0.0001088586,0.001789543,0.00009152292,0.0001082594],"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":[0.0001378969,0.0007044091,0.0002300351,0.0002149387,0.00009521774,0.00000815781,0.0004206762,0.4582195,0.01986433,0.4716558,0.0003679739,0.048081],"study_design_scores_gemma":[0.001244477,0.00005800674,0.000535856,0.00005531078,0.00004340935,0.000005566041,0.001120554,0.950862,0.03196341,0.01179965,0.001585376,0.0007264132],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7952295,0.0002828595,0.1881185,0.0006447926,0.000232926,0.0009168696,0.00006286023,0.00278883,0.01172283],"genre_scores_gemma":[0.8524886,0.0001527359,0.1465514,0.000139831,0.00002001037,0.0002097231,0.0003344265,0.00009111638,0.00001210864],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4926425,"threshold_uncertainty_score":0.9998885,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03553269693070876,"score_gpt":0.2578236512061715,"score_spread":0.2222909542754627,"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."}}