{"id":"W4407624534","doi":"10.1021/acssensors.4c03451","title":"Using Machine Learning and Optical Microscopy Image Analysis of Immunosensors Made on Plasmonic Substrates: Application to Detect the SARS-CoV-2 Virus","year":2025,"lang":"en","type":"article","venue":"ACS Sensors","topic":"Biosensors and Analytical Detection","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Innovation Cluster (Canada)","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Fundação de Amparo à Pesquisa do Estado de São Paulo","keywords":"Microscopy; Plasmon; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Coronavirus disease 2019 (COVID-19); Nanotechnology; Materials science; Optics; Virology; Optoelectronics; Physics; Biology; Medicine; Pathology; Infectious disease (medical specialty)","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":[0.0004023531,0.0006638387,0.0003808079,0.0006490592,0.0001648344,0.000479538,0.0003957819,0.0007169389,0.0006388476],"category_scores_gemma":[0.0005989593,0.000222329,0.0003575099,0.0003672934,0.0003300262,0.0003884394,0.0002654863,0.0003800888,0.0002891727],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003585949,"about_ca_system_score_gemma":0.0002553999,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006379478,"about_ca_topic_score_gemma":0.001091664,"domain_scores_codex":[0.9996603,0.00007000924,0.00001817737,0.0000937474,0.0001212511,0.00003662424],"domain_scores_gemma":[0.9997939,0.00007660766,0.00004530978,0.00002119899,0.00005006743,0.00001287217],"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":[0.0001218992,0.00008779874,0.001353458,0.0001667358,0.00003051231,0.0001018922,0.00002378622,0.003069195,0.9337613,0.0004589182,0.0002394083,0.0605852],"study_design_scores_gemma":[0.00001319043,0.0002681982,0.005979932,0.00001498647,0.00003530332,0.0004293318,0.00002353687,0.1744602,0.8159755,0.0004393775,0.002325259,0.00003511177],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4528361,0.002332286,0.5388894,0.00051529,0.0001784744,0.0001235615,0.0002454655,0.001398859,0.003480569],"genre_scores_gemma":[0.7014703,0.0009320342,0.2953837,0.0002120053,0.0000585027,0.0001010351,0.0001695735,0.00004142998,0.00163127],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0007169389,"threshold_uncertainty_score":0.002601802,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01207848487233193,"score_gpt":0.2794142719101899,"score_spread":0.267335787037858,"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."}}