{"id":"W3128633993","doi":"10.1364/oe.417272","title":"AI-optimised tuneable sources for bandwidth-scalable, sub-nanosecond wavelength switching","year":2021,"lang":"en","type":"article","venue":"Optics Express","topic":"Optical Network Technologies","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Infineon Technologies (Canada)","funders":"Engineering and Physical Sciences Research Council; Royal Academy of Engineering; Microsoft Research","keywords":"Bandwidth (computing); Nanosecond; Optical switch; Optoelectronics; Wavelength; Optical amplifier; Wideband; Materials science; Optics; Laser; Terahertz radiation; Computer science; Telecommunications; Physics","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":[0.0001372874,0.0002980596,0.0003825609,0.00007687019,0.0001460032,0.0002210927,0.0003486401,0.0002930419,0.0000808],"category_scores_gemma":[0.0001644888,0.0003131417,0.0001283653,0.0002381745,0.00005352819,0.0002306667,0.0001583008,0.0003817564,0.00002916176],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005585657,"about_ca_system_score_gemma":0.00003115505,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001206297,"about_ca_topic_score_gemma":0.000004359877,"domain_scores_codex":[0.9983686,0.00001531234,0.0003521551,0.0003761936,0.000189643,0.0006980638],"domain_scores_gemma":[0.9987937,0.0003341074,0.00003838628,0.0005737927,0.0001365984,0.0001233886],"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":[0.00007164027,0.0002598557,0.0002202932,0.001234892,0.0005058775,0.0001466695,0.0005500157,0.4827454,0.3922612,0.04987386,0.0236177,0.04851254],"study_design_scores_gemma":[0.0007616016,0.00004351816,0.00002396658,0.00009981589,0.00004913272,0.00001183618,0.0002128888,0.5358439,0.4369271,0.00397061,0.02154745,0.0005081817],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8331547,0.002598392,0.1414523,0.0006173817,0.001303889,0.0006837861,0.00007533794,0.00246613,0.01764805],"genre_scores_gemma":[0.7979547,0.0005520193,0.199846,0.0001773354,0.0002824172,0.0001431204,0.00004090767,0.0001490323,0.0008544709],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05839376,"threshold_uncertainty_score":0.9999321,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01070330901504082,"score_gpt":0.2132091867636041,"score_spread":0.2025058777485632,"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."}}