{"id":"W3213675005","doi":"10.1364/nlo.2021.nf2a.1","title":"All-optical Sampling for Adaptive On-Chip Picosecond Pulse-Shaping","year":2021,"lang":"en","type":"preprint","venue":"","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":"Pulse shaping; Picosecond; Coherence (philosophical gambling strategy); Waveform; Chip; Computer science; Ultrashort pulse; Sampling (signal processing); Electronic engineering; Power (physics); Pulse (music); Coherence time; Nonlinear system; Optics; Physics; Telecommunications; Engineering","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":[0.0001502494,0.0002866529,0.0001953343,0.0001675257,0.0001997484,0.000479876,0.0005184987,0.0002223148,0.001260397],"category_scores_gemma":[0.0004619901,0.0001433521,0.0001048971,0.0002214793,0.0003859324,0.0002986456,0.0005446089,0.0003454564,0.000316082],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000383098,"about_ca_system_score_gemma":0.0004776747,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004602796,"about_ca_topic_score_gemma":0.0009554665,"domain_scores_codex":[0.9998292,0.00002246514,0.000006422115,0.00003563035,0.0000799039,0.00002643819],"domain_scores_gemma":[0.9997482,0.00008366561,0.00003806808,0.00007128623,0.00003288353,0.00002597474],"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":[0.0002596512,0.0001281165,0.000757204,0.0001365261,0.0000232058,0.00009889321,0.0001069334,0.01934932,0.854543,0.01531261,0.0006960784,0.1085884],"study_design_scores_gemma":[0.00003505137,0.0002308203,0.000679858,0.00001045579,0.00001522628,0.0001545371,0.00001947765,0.309527,0.6776011,0.002627969,0.009069578,0.00002907602],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3974824,0.0005867905,0.586458,0.0001867682,0.0001382775,0.0001215958,0.0001865295,0.002033305,0.01280647],"genre_scores_gemma":[0.8818955,0.0001714453,0.1154077,0.00007460781,0.00002897099,0.00005251464,0.00009899051,0.00009432148,0.002175859],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001260397,"threshold_uncertainty_score":0.004216433,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1026082843545596,"score_gpt":0.3282683328509723,"score_spread":0.2256600484964127,"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."}}