{"id":"W4396892844","doi":"10.1364/ofc.2024.m3i.2","title":"Recalibration Learning: Enabling Universal Transfer of ML Model of Gain and NF for Remote Optically Pumped Amplifiers","year":2024,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Reservoir Computing","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Nokia (Canada)","funders":"","keywords":"Amplifier; Optical amplifier; Computer science; Optical pumping; Optoelectronics; Materials science; Electronic engineering; Remote sensing; Physics; Engineering; Telecommunications; Optics; Geology; Bandwidth (computing); Laser","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":[0.0008480468,0.0006939681,0.0003942164,0.0001865014,0.0002888165,0.0006772617,0.001671029,0.0008453843,0.001649808],"category_scores_gemma":[0.003501473,0.0003196805,0.0003285827,0.0001654487,0.001364711,0.00190177,0.001795188,0.001816148,0.0005983411],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004526202,"about_ca_system_score_gemma":0.0005490434,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001109941,"about_ca_topic_score_gemma":0.00165702,"domain_scores_codex":[0.999396,0.0001248138,0.00002465529,0.0001724046,0.0002220816,0.00006011247],"domain_scores_gemma":[0.9989359,0.0003364299,0.0001594788,0.000436122,0.00008908245,0.00004307667],"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":[0.000366657,0.0003191234,0.00222345,0.0001649771,0.0000995701,0.0002702124,0.0004905398,0.3971981,0.352633,0.02367826,0.001702247,0.2208539],"study_design_scores_gemma":[0.000009717616,0.00008430312,0.0002527029,0.00001056545,0.000006472056,0.00008052772,0.00001654123,0.8772462,0.1147071,0.006600176,0.0009567474,0.00002881907],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.07215242,0.00006794358,0.9236442,0.0002502707,0.00003365434,0.00003647991,0.00003575414,0.001930174,0.001849064],"genre_scores_gemma":[0.8573379,0.00006258441,0.1400837,0.0001718132,0.00001857606,0.00007095782,0.00006103875,0.0002537956,0.001939641],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001671029,"threshold_uncertainty_score":0.005519092,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0277763694310706,"score_gpt":0.2488874831519073,"score_spread":0.2211111137208366,"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."}}