{"id":"W2028031219","doi":"10.3390/mi6010063","title":"Multiplex, Quantitative, Reverse Transcription PCR Detection of Influenza Viruses Using Droplet Microfluidic Technology","year":2014,"lang":"en","type":"article","venue":"Micromachines","topic":"Electrowetting and Microfluidic Technologies","field":"Engineering","cited_by":41,"is_retracted":false,"has_abstract":true,"ca_institutions":"Provincial Laboratory of Public Health; University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; CMC Microsystems","keywords":"Multiplex; Microfluidics; Electrowetting; Reverse transcription polymerase chain reaction; Real-time polymerase chain reaction; Multiplexing; Reverse transcriptase; Detection limit; Influenza A virus; Nanotechnology; Materials science; Virology; RNA; Virus; Biology; Chromatography; Chemistry; Computer science; Optoelectronics; Bioinformatics; Messenger RNA; Gene","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001289417,0.0006982952,0.0006472082,0.0006060644,0.0002200029,0.0007861183,0.0005170139,0.0004873373,0.0008498058],"category_scores_gemma":[0.0008769133,0.0003822759,0.0003404889,0.0002930282,0.0003738113,0.0005668341,0.0004967829,0.0005585619,0.0003978881],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003655036,"about_ca_system_score_gemma":0.0003273536,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001617607,"about_ca_topic_score_gemma":0.0004188008,"domain_scores_codex":[0.9987994,0.000158122,0.0001085202,0.0004829527,0.0003777897,0.00007320988],"domain_scores_gemma":[0.9996216,0.0001886807,0.00006635712,0.00004701029,0.00005698399,0.00001933048],"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":[0.000047053,0.00003030713,0.0003494344,0.00006971267,0.000009576112,0.00002567089,0.00002993082,0.0002988395,0.990646,0.0002239951,0.0001341239,0.008135463],"study_design_scores_gemma":[0.000009872754,0.0001741452,0.0007095353,0.000006443542,0.00001591093,0.0001100528,0.00001077741,0.006080561,0.9905866,0.0001437685,0.002137733,0.00001458823],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.3559797,0.004430647,0.6322531,0.0003170102,0.0004420288,0.0006522745,0.00192482,0.002028889,0.001971597],"genre_scores_gemma":[0.415617,0.002398738,0.5762275,0.0002215009,0.0001137458,0.001012516,0.0008914875,0.00006570978,0.003451733],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001289417,"threshold_uncertainty_score":0.006819129,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02097818500077169,"score_gpt":0.2557083592585473,"score_spread":0.2347301742577756,"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."}}