{"id":"W3024814366","doi":"10.1149/ma2020-01161102mtgabs","title":"(Invited) Nanoscale Llight Management Using Three-Dimensional Scanning Near-Field Optical Microscopy","year":2020,"lang":"en","type":"article","venue":"ECS Meeting Abstracts","topic":"Near-Field Optical Microscopy","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Near-field scanning optical microscope; Materials science; Aperture (computer memory); Optical microscope; Plasmon; Photonic crystal; Near and far field; Nanophotonics; Optics; Photonics; Nanotechnology; Nanoscopic scale; Optical phenomena; Optoelectronics; Scanning electron microscope; Physics; Acoustics","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":[0.0002221213,0.0003655626,0.0003452692,0.00005840202,0.0002371594,0.0002334819,0.0003133806,0.0002316912,0.0001060378],"category_scores_gemma":[0.0001645697,0.0003981088,0.0001227,0.0002956117,0.00007645784,0.0001728832,0.0001846373,0.0006261748,0.0003137307],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000856988,"about_ca_system_score_gemma":0.00001916319,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004102567,"about_ca_topic_score_gemma":0.000006600064,"domain_scores_codex":[0.9977681,0.00001918831,0.0005731968,0.0004982885,0.0003865835,0.0007546566],"domain_scores_gemma":[0.9989212,0.0002408963,0.00007069665,0.0003003153,0.00004857478,0.00041831],"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":[0.00004236964,0.00003990494,0.0006352998,0.0002227758,0.00007899265,0.0002088006,0.0001489967,0.3368888,0.6530289,0.00001562621,0.008266161,0.0004233199],"study_design_scores_gemma":[0.0005041467,0.0000852029,0.001257209,0.0004389775,0.0000728089,0.00002215256,0.00004174227,0.145439,0.8430821,0.00006190805,0.00841756,0.0005771898],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9688221,0.0003599588,0.001143518,0.002477776,0.000733278,0.0002729923,0.000005460475,0.0005614278,0.02562347],"genre_scores_gemma":[0.8638868,0.000007467882,0.1333139,0.00232028,0.0003367311,0.000007695884,0.00000636394,0.00009584177,0.00002485745],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1914498,"threshold_uncertainty_score":0.9998471,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01557175351973438,"score_gpt":0.2425845325655556,"score_spread":0.2270127790458213,"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."}}