{"id":"W4407736613","doi":"10.1109/jsac.2025.3543548","title":"Minimizing Fiber’s Nonlinear Interference Noise by Designing Launched Signal PSD","year":2025,"lang":"en","type":"article","venue":"IEEE Journal on Selected Areas in Communications","topic":"Advanced Fiber Optic Sensors","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Huawei Technologies (Canada); University of Alberta","funders":"","keywords":"Computer science; Launched; Interference (communication); Phase noise; Telecommunications; Noise (video); Nonlinear system; SIGNAL (programming language); Electronic engineering; Electrical engineering; Physics; Artificial intelligence; Engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004777241,0.0006787896,0.0003217367,0.000290764,0.0002166521,0.0005189404,0.0003783918,0.0004759886,0.000429251],"category_scores_gemma":[0.001203054,0.000208736,0.0002431531,0.0003848391,0.0005269214,0.0006346112,0.000449205,0.0003133475,0.0002101881],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006956785,"about_ca_system_score_gemma":0.0008794914,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005387132,"about_ca_topic_score_gemma":0.0007685887,"domain_scores_codex":[0.9997122,0.00005486434,0.00001429469,0.00005426586,0.0001250588,0.00003938295],"domain_scores_gemma":[0.9996265,0.0001387155,0.0001165282,0.00003151427,0.00007263321,0.00001398405],"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":[0.00009059238,0.0001498648,0.001210658,0.0002697122,0.00004972909,0.0001419393,0.0001415659,0.5889055,0.3388281,0.03032225,0.0005022992,0.03938781],"study_design_scores_gemma":[0.00001762539,0.000194909,0.0005105889,0.00002047182,0.00002448721,0.0001147737,0.00003212252,0.8890315,0.1044933,0.003361857,0.002176426,0.00002195216],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.10532,0.000277259,0.8870296,0.0001565569,0.00002273123,0.00007175113,0.0000378113,0.0001437916,0.006940466],"genre_scores_gemma":[0.7936567,0.0004924588,0.2040849,0.00006458982,0.00002152166,0.00009568597,0.00003547563,0.00006902829,0.001479598],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0006956785,"threshold_uncertainty_score":0.0050475,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02386731909889466,"score_gpt":0.283094695595521,"score_spread":0.2592273764966264,"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."}}