{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000162391,0.0001940632,0.000313442,0.0003517715,0.0001289694,0.00005726337,0.00010358,0.0001180177,0.000003955116],"category_scores_gemma":[0.00007123619,0.0001574681,0.0001151289,0.001202733,0.00009161135,0.00004299573,0.00003268187,0.0003144693,0.00001343723],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006702243,"about_ca_system_score_gemma":0.00000644539,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002206018,"about_ca_topic_score_gemma":0.00009024218,"domain_scores_codex":[0.9990035,0.00005056643,0.0003140984,0.0002557652,0.0001315238,0.0002445245],"domain_scores_gemma":[0.9994427,0.0001915445,0.00004561036,0.0002384663,0.00004458998,0.0000371183],"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":[0.00004111068,0.0000101111,0.0002248143,0.00002523001,0.0003148611,0.000001882267,0.00005865826,0.0408428,0.9567043,0.00009241703,0.000005258288,0.001678576],"study_design_scores_gemma":[0.0000940247,0.00003113895,0.002373625,0.00001948083,0.0003280385,0.000002106299,0.00005570054,0.3101617,0.6866492,0.00003183304,0.0001466326,0.0001065084],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9941185,0.0002134098,0.004932658,0.00008739995,0.0000472645,0.0001635997,0.00001452974,0.00009532226,0.0003273071],"genre_scores_gemma":[0.9981169,0.0001371865,0.001597499,0.00005724602,0.00001334547,0.000005156272,0.00000529274,0.00002298545,0.00004436041],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2700551,"threshold_uncertainty_score":0.6421363,"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."}}