{"id":"W2938807164","doi":"10.1051/epjconf/201920501012","title":"Peak power &amp; average power scaling via fourier domain OPA (FOPA)","year":2019,"lang":"en","type":"article","venue":"EPJ Web of Conferences","topic":"Laser-Matter Interactions and Applications","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa; Joint Attosecond Science Laboratory; Institut National de la Recherche Scientifique","funders":"","keywords":"Fourier transform; Boosting (machine learning); Scaling; Power (physics); Fourier domain; Frequency domain; Physics; Computer science; Optics; Materials science; Mathematics; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002766383,0.000789052,0.0002498054,0.0005823403,0.0004464928,0.001059182,0.0007662504,0.0003760855,0.01334657],"category_scores_gemma":[0.001125358,0.0002327769,0.0001990916,0.0005566757,0.000543565,0.001004406,0.0005372193,0.0007950686,0.002810109],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005238538,"about_ca_system_score_gemma":0.0003736258,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006616924,"about_ca_topic_score_gemma":0.001034118,"domain_scores_codex":[0.9996772,0.00002172751,0.00001584216,0.00008470243,0.0001394137,0.00006110099],"domain_scores_gemma":[0.9995455,0.0001280024,0.00009153922,0.0000979688,0.0001117362,0.00002517321],"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":[0.0001682293,0.0001922843,0.0009266972,0.000138121,0.00002174359,0.0001334899,0.0002241232,0.002831558,0.8694625,0.01334526,0.003420287,0.1091356],"study_design_scores_gemma":[0.00001988824,0.0001582585,0.001796011,0.00002069348,0.00001420325,0.0002403144,0.0000560095,0.0699752,0.8988428,0.004214382,0.02462078,0.0000414752],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3963391,0.0005819762,0.5059565,0.001438717,0.0004725426,0.000256582,0.001241484,0.01274552,0.08096758],"genre_scores_gemma":[0.8525879,0.0002702607,0.1292115,0.0001938452,0.00008258931,0.0001852057,0.0002951567,0.00109029,0.0160832],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01334657,"threshold_uncertainty_score":0.04464877,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009977612475968067,"score_gpt":0.2567745174128439,"score_spread":0.2467969049368758,"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."}}