{"id":"W2791124711","doi":"10.1364/ofc.2018.m2j.2","title":"Dual repetition-rate laser based on in-cavity fractional temporal self-imaging for low-noise RF signal generation","year":2018,"lang":"en","type":"article","venue":"Optical Fiber Communication Conference","topic":"Advanced Fiber Laser Technologies","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Laser; Noise (video); Optics; Radio frequency; Relative intensity noise; Phase noise; SIGNAL (programming language); Repetition (rhetorical device); Dispersion (optics); Physics; Computer science; Semiconductor laser theory; Acoustics; Telecommunications; Artificial intelligence; Image (mathematics)","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":[0.0004109506,0.000329471,0.0003674657,0.0004475733,0.0004148587,0.0004261079,0.001186417,0.0005552497,0.001120186],"category_scores_gemma":[0.0004521824,0.0002908217,0.0002353379,0.0002675165,0.000611054,0.0008035401,0.0007286841,0.0006286624,0.0004520408],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004184288,"about_ca_system_score_gemma":0.0003349083,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000333486,"about_ca_topic_score_gemma":0.0006956964,"domain_scores_codex":[0.9995297,0.00007162511,0.00002298021,0.0001189693,0.0001940873,0.0000626207],"domain_scores_gemma":[0.9995674,0.00008412698,0.0001005284,0.00009432195,0.00009843398,0.00005511166],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00009091147,0.00004986971,0.0003008134,0.00004502427,0.000006274159,0.00007209757,0.00006488419,0.0004510056,0.9884766,0.003369183,0.0001518949,0.006921409],"study_design_scores_gemma":[0.00005085428,0.0003216518,0.000711748,0.00000898277,0.00001895146,0.000555158,0.0000196489,0.03594698,0.9578782,0.0007021281,0.003738148,0.00004759205],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8208421,0.000613731,0.1700812,0.0003497272,0.0001425904,0.0001133614,0.0001022613,0.001018543,0.006736506],"genre_scores_gemma":[0.8732355,0.0001571648,0.1239616,0.00006811162,0.0000443976,0.00006825376,0.00004335255,0.00003799839,0.002383668],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001186417,"threshold_uncertainty_score":0.003747404,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02644910589989073,"score_gpt":0.2850411425302488,"score_spread":0.2585920366303581,"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."}}