{"id":"W4283696421","doi":"10.3390/bios12070466","title":"Highly Sensitive Flexible SERS-Based Sensing Platform for Detection of COVID-19","year":2022,"lang":"en","type":"article","venue":"Biosensors","topic":"Biosensors and Analytical Detection","field":"Engineering","cited_by":54,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Nanotechnology; Multiplex; Flexibility (engineering); Context (archaeology); Computer science; Surface-enhanced Raman spectroscopy; Substrate (aquarium); Coronavirus disease 2019 (COVID-19); Fingerprint (computing); Materials science; Raman spectroscopy; Infectious disease (medical specialty); Disease; Bioinformatics; Medicine; Artificial intelligence; Biology; Raman scattering; 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":[0.0004897115,0.0008560299,0.0004040963,0.0005361218,0.0003366433,0.0004790871,0.0008072628,0.001087238,0.001348506],"category_scores_gemma":[0.0004853531,0.0004469099,0.0004256114,0.0003507391,0.0003682539,0.0008323919,0.000667281,0.0008333443,0.0007638948],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002746437,"about_ca_system_score_gemma":0.0002351815,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003657814,"about_ca_topic_score_gemma":0.0006957594,"domain_scores_codex":[0.9994628,0.00007276121,0.00002804205,0.0001614928,0.0002071833,0.00006772899],"domain_scores_gemma":[0.9998337,0.00004257741,0.00003210683,0.00001576638,0.0000521759,0.00002364124],"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":[0.00004672455,0.00002354649,0.00009613368,0.0001265641,0.00001057827,0.0001414068,0.0000358925,0.0003820507,0.9917737,0.0003685641,0.0003764836,0.006618447],"study_design_scores_gemma":[0.00001828416,0.0003380477,0.0007824946,0.00002023978,0.00002074045,0.0003765701,0.0000642659,0.01137815,0.9803925,0.0002531161,0.006303056,0.00005256056],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7069411,0.01473368,0.2520656,0.001354658,0.00109115,0.0004309566,0.0009024463,0.002252889,0.02022746],"genre_scores_gemma":[0.8725425,0.004528847,0.1140972,0.000611604,0.0001119019,0.0002730045,0.0005959413,0.00007463549,0.007164327],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001348506,"threshold_uncertainty_score":0.004511178,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02336580431182011,"score_gpt":0.2442747810553289,"score_spread":0.2209089767435088,"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."}}