{"id":"W4405362220","doi":"10.1109/aiotsys63104.2024.10780644","title":"Memristor Based Gain-Scheduling Controller for Erbium-Doped Fiber Amplifiers","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Erbium doped fiber amplifier; Erbium; Computer science; Gain scheduling; Fiber amplifier; Materials science; Memristor; Optical amplifier; Amplifier; Optoelectronics; Doping; Electronic engineering; CMOS; Engineering; Control (management); Physics; Optics; 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":[0.0001231816,0.0001495397,0.0001640242,0.00006309911,0.00007190448,0.00004830217,0.00007608972,0.00005190868,0.0002593519],"category_scores_gemma":[0.00003527281,0.0001317221,0.0001186051,0.0001104157,0.00001244395,0.00009334264,0.000009985883,0.0001427604,0.0001499973],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005540553,"about_ca_system_score_gemma":0.00001381491,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":6.573733e-7,"about_ca_topic_score_gemma":9.44571e-7,"domain_scores_codex":[0.9992736,0.000008481366,0.0001777815,0.0002005857,0.00007134717,0.0002681796],"domain_scores_gemma":[0.9994389,0.0003546942,0.000009273459,0.0001023114,0.0000223378,0.00007247883],"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":[0.00004086453,0.00000627747,0.000002155951,0.0003210215,0.00005847631,0.00001266496,0.00006776825,0.9148065,0.06040527,0.001798899,0.004612715,0.01786732],"study_design_scores_gemma":[0.0004861413,0.000024532,0.000001583384,0.00005710741,0.00002160378,0.00000246269,0.00002655487,0.8577605,0.05260232,0.0003692007,0.08844024,0.0002077085],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01612864,0.0007653029,0.9735011,0.0001295203,0.0006706842,0.0003524909,0.000008414739,0.001337396,0.007106392],"genre_scores_gemma":[0.9767864,0.000003851694,0.01933778,0.0002903806,0.0003030592,0.00004461817,0.000009447931,0.00005760329,0.003166894],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9606577,"threshold_uncertainty_score":0.537147,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02023867395804399,"score_gpt":0.2577493073810571,"score_spread":0.2375106334230131,"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."}}