{"id":"W4290002424","doi":"","title":"Surface nanostructuring for improved resolution in surface plasmon resonance imaging","year":2018,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Nanofabrication and Lithography Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut interdisciplinaire d'innovation technologique; Université de Sherbrooke","funders":"","keywords":"Surface plasmon resonance; Surface plasmon; Materials science; Localized surface plasmon; Image resolution; Resolution (logic); Surface (topology); Plasmon; Nanophotonics; Nanotechnology; Resonance (particle physics); Optoelectronics; Optics; Nanoparticle; Physics; Computer science","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":[0.000275799,0.0004563526,0.000438088,0.0002996403,0.0002529865,0.000789261,0.0004809524,0.0008324228,0.003947369],"category_scores_gemma":[0.0005911932,0.000325012,0.0002740899,0.0003291336,0.0004048563,0.0006667576,0.0004565576,0.000773956,0.001230318],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003155792,"about_ca_system_score_gemma":0.0001490288,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002575232,"about_ca_topic_score_gemma":0.0004087833,"domain_scores_codex":[0.9997409,0.00002687227,0.00001329053,0.00006708357,0.000116091,0.00003571995],"domain_scores_gemma":[0.9996275,0.0001721258,0.00005076325,0.00006545963,0.00006368589,0.00002051027],"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":[0.00002437538,0.000016387,0.00003154191,0.00007806744,0.000003418614,0.0000337462,0.00002335314,0.000598163,0.9920134,0.0007016464,0.000249685,0.006226207],"study_design_scores_gemma":[0.000008990229,0.0000590817,0.0003463412,0.000005471132,0.000007044059,0.00009414228,0.00001313443,0.0125833,0.9831033,0.0004914001,0.003279166,0.000008565766],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7241421,0.01195056,0.2321545,0.002309492,0.001092075,0.0001205942,0.0003215525,0.002981385,0.02492773],"genre_scores_gemma":[0.8773037,0.003064777,0.1047558,0.000393645,0.0002301504,0.00003920144,0.0002138664,0.0007480121,0.01325087],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003947369,"threshold_uncertainty_score":0.01320529,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009223603791945057,"score_gpt":0.2205842094952636,"score_spread":0.2113606057033186,"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."}}