{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001863426,0.0002568254,0.0003325673,0.0005486731,0.0001110955,0.00001643464,0.0002285714,0.0002670587,0.000005847296],"category_scores_gemma":[0.0001067589,0.0002583308,0.00009445458,0.0005579253,0.0001839714,0.0001377498,0.00003143518,0.0002756682,0.0000135456],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000064799,"about_ca_system_score_gemma":0.000009547434,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002327633,"about_ca_topic_score_gemma":0.0000437202,"domain_scores_codex":[0.9988949,0.00003695142,0.0004072826,0.0002512561,0.00008433124,0.0003253225],"domain_scores_gemma":[0.9994483,0.00005382667,0.00008893478,0.0003136498,0.00006858014,0.00002670025],"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":[0.00002199993,0.00002389633,0.001519977,0.0001202291,0.00004796424,0.000001060279,0.0001758084,0.0001279561,0.9876334,0.0001729909,0.0001697174,0.009984994],"study_design_scores_gemma":[0.0005101045,0.0001219973,0.0003068698,0.00007888199,0.00004104323,0.00003575689,0.000129851,0.001786622,0.9780474,0.0005698486,0.01811919,0.0002524358],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9459443,0.009699824,0.04261902,0.00004241338,0.0002337791,0.0001519071,0.00001716652,0.001252766,0.00003884702],"genre_scores_gemma":[0.9938685,0.0005042056,0.005462357,0.00005424652,0.00002717931,0.0000113198,0.000004209003,0.00005531832,0.00001262032],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04792426,"threshold_uncertainty_score":0.9999869,"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."}}