{"id":"W2068968026","doi":"10.1039/c4lc01468e","title":"Rapid and multiplex detection of Legionella's RNA using digital microfluidics","year":2015,"lang":"en","type":"article","venue":"Lab on a Chip","topic":"Biosensors and Analytical Detection","field":"Engineering","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada; McGill University","funders":"National Research Council Canada; Genome Canada","keywords":"Legionella; Multiplex; Microfluidics; Digital microfluidics; Computational biology; Digital polymerase chain reaction; Biology; Bacteria; Microbiology; Nanotechnology; Genetics; Chemistry; Polymerase chain reaction; Gene; Materials science","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":[0.0007896869,0.0004952883,0.0003123703,0.0008865767,0.0001940565,0.0006131998,0.0004811564,0.0004254145,0.0006556532],"category_scores_gemma":[0.0008754389,0.0002540951,0.0002753875,0.0002791348,0.0003763368,0.0005979691,0.000503362,0.0003017874,0.0002657206],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005149539,"about_ca_system_score_gemma":0.0002945468,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002257278,"about_ca_topic_score_gemma":0.0004808481,"domain_scores_codex":[0.9993717,0.00008938747,0.00005442097,0.0001930974,0.000251317,0.00004001615],"domain_scores_gemma":[0.9997733,0.0001054761,0.00003901533,0.00001925121,0.00004218616,0.00002081663],"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":[0.00007744387,0.00004743041,0.0007769242,0.0001921408,0.00001971177,0.00005968599,0.00005510631,0.0004762911,0.9692541,0.0008392566,0.0003411391,0.02786089],"study_design_scores_gemma":[0.0000173045,0.0001634156,0.001087265,0.00001441462,0.00002081322,0.0001916898,0.0000213536,0.005004026,0.9861941,0.0003220062,0.006937874,0.00002576426],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5577022,0.02240275,0.4031117,0.001096751,0.001176318,0.0006528144,0.001759131,0.002503201,0.009595088],"genre_scores_gemma":[0.5836185,0.006453792,0.4029717,0.0004098852,0.0002342844,0.0004261872,0.0005634081,0.00003084579,0.005291406],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0008865767,"threshold_uncertainty_score":0.004176319,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03009792894697116,"score_gpt":0.213368479621879,"score_spread":0.1832705506749079,"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."}}