{"id":"W4289712575","doi":"10.5772/intechopen.105950","title":"Perspective Chapter: Microfluidic Technologies for On-Site Detection and Quantification of Infectious Diseases – The Experience with SARS-CoV-2/COVID-19","year":2022,"lang":"en","type":"book-chapter","venue":"Infectious diseases","topic":"Biosensors and Analytical Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Pandemic; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Preparedness; Coronavirus disease 2019 (COVID-19); Diagnostic test; Medicine; Personal protective equipment; 2019-20 coronavirus outbreak; Molecular diagnostics; Transmission (telecommunications); Turnaround time; Intensive care medicine; Biosecurity; Health care; Virology; Risk analysis (engineering); Infectious disease (medical specialty); Computer science; Engineering; Operations management; Pathology; Disease; Bioinformatics; Emergency medicine; Telecommunications","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":[0.00005795166,0.0004480171,0.0004150297,0.0004018448,0.0004830896,0.00007904391,0.000144207,0.0002037285,0.00003406919],"category_scores_gemma":[0.000198776,0.0003531739,0.0002247177,0.0001506061,0.0005177014,0.0001322316,0.00006104146,0.0003293165,0.00000633842],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004595844,"about_ca_system_score_gemma":0.00004849417,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002056629,"about_ca_topic_score_gemma":0.0001598696,"domain_scores_codex":[0.9985866,0.00002214638,0.0003172608,0.0005581633,0.0002710823,0.0002447697],"domain_scores_gemma":[0.9988386,0.0002647956,0.0001956432,0.0004688483,0.0001469905,0.00008514118],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.003278949,0.001443766,0.0100784,0.006207329,0.005231632,0.0001415362,0.00610983,0.01484877,0.1688458,0.6357191,0.003432335,0.1446626],"study_design_scores_gemma":[0.009479523,0.01311972,0.008029831,0.001371362,0.007420276,0.0007634393,0.006025615,0.01604637,0.1582463,0.2127758,0.5577602,0.008961605],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7070914,0.0817387,0.07989258,0.002419298,0.004757618,0.01633031,0.01071309,0.0202102,0.07684682],"genre_scores_gemma":[0.9956581,0.003246428,0.000003120359,0.00009526826,0.00008545819,0.0003631074,0.00002920894,0.00009566569,0.0004236466],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5543278,"threshold_uncertainty_score":0.999892,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01641172149630795,"score_gpt":0.2501877214008592,"score_spread":0.2337759999045513,"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."}}