{"id":"W2515561997","doi":"10.1109/pn.2016.7537900","title":"Evidence of optical rectification in Ag nanoparticles and its application in rectenna device","year":2016,"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":"Queen's University","funders":"","keywords":"Rectenna; Rectification; Optical rectification; Optoelectronics; Nanoparticle; Substrate (aquarium); Materials science; Nonlinear optical; Colloidal gold; Nonlinear optics; Optical power; Nanotechnology; Nonlinear system; Optics; Electrical engineering; Engineering; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001752842,0.0002303365,0.0001520307,0.0002416757,0.0002592232,0.0002910241,0.0004302851,0.0007989727,0.003194248],"category_scores_gemma":[0.0006553299,0.0001557247,0.0001401356,0.0001543671,0.0003750113,0.0003780219,0.0002112715,0.0006388948,0.0004723708],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001260295,"about_ca_system_score_gemma":0.00008705688,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001753701,"about_ca_topic_score_gemma":0.0001640579,"domain_scores_codex":[0.9998654,0.00001481737,0.000007039336,0.00003919631,0.00004898389,0.00002451425],"domain_scores_gemma":[0.9996336,0.0001541623,0.00005530185,0.00006957103,0.00006613111,0.0000212805],"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":[0.0001567606,0.00002170517,0.0002695512,0.00008473042,0.000004225517,0.000450072,0.00008398097,0.0001069827,0.9942809,0.001299258,0.0002315569,0.003010185],"study_design_scores_gemma":[0.00001442785,0.0001458307,0.001492458,0.000008138501,0.000006552567,0.0005325196,0.00006186008,0.002503532,0.99325,0.0006591084,0.00131999,0.000005625673],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9635515,0.001813551,0.01328935,0.0009539632,0.0001313331,0.00003359555,0.0001534582,0.000462554,0.01961077],"genre_scores_gemma":[0.9949935,0.0001810523,0.002642497,0.00005765729,0.0000107691,0.000009509638,0.00004141303,0.0000179668,0.002045631],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003194248,"threshold_uncertainty_score":0.0106858,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04765069424981837,"score_gpt":0.2828466893397304,"score_spread":0.235195995089912,"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."}}