{"id":"W4407118283","doi":"10.1364/oe.545301","title":"Modeling the second stage of extended L-band fiber amplifiers using neural networks trained on experimental data","year":2025,"lang":"en","type":"article","venue":"Optics Express","topic":"Optical Network Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Optics; Stage (stratigraphy); Artificial neural network; Optical fiber; Amplifier; Materials science; Computer science; Telecommunications; Physics; Artificial intelligence; Bandwidth (computing)","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001235091,0.0001891239,0.0002163207,0.00006266723,0.00008168036,0.00006181739,0.0007088908,0.0001369558,0.00006909544],"category_scores_gemma":[0.00002857552,0.0001551331,0.00004398567,0.0001770561,0.0001169614,0.0001311083,0.0002485146,0.0003252136,8.623344e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004023265,"about_ca_system_score_gemma":0.000008840912,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000038153,"about_ca_topic_score_gemma":0.000002586905,"domain_scores_codex":[0.9990067,0.00002045077,0.0002757994,0.00024879,0.0001339856,0.000314315],"domain_scores_gemma":[0.9988078,0.0001326391,0.00002689399,0.0009765981,0.00002271937,0.00003333013],"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":[0.000020465,0.00002423158,0.000003080813,0.00003936255,0.00004995787,0.000002907079,0.0000564061,0.9895468,0.006942014,0.00236085,0.0002244567,0.0007294943],"study_design_scores_gemma":[0.0002609744,0.00002181573,0.000002330994,0.000048212,0.00001500352,8.303153e-7,0.0004249598,0.9879588,0.01066414,0.0001054358,0.000354722,0.0001427913],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9332161,0.0007533623,0.06113058,0.00002800824,0.0003371474,0.0002725961,0.00007708501,0.0002589892,0.003926117],"genre_scores_gemma":[0.9914348,0.00001659194,0.007922317,0.0000358984,0.00005318197,0.00001030644,0.00001370767,0.00003214487,0.0004810881],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05821865,"threshold_uncertainty_score":0.6326146,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04087995421258121,"score_gpt":0.2837839295822382,"score_spread":0.242903975369657,"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."}}