{"id":"W2617743775","doi":"10.1021/acs.analchem.7b01315","title":"Microfluidic Capillaric Circuit for Rapid and Facile Bacteria Detection","year":2017,"lang":"en","type":"article","venue":"Analytical Chemistry","topic":"Microfluidic and Capillary Electrophoresis Applications","field":"Engineering","cited_by":64,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; McGill University and Génome Québec Innovation Centre","funders":"Fonds de recherche du Québec – Nature et technologies; Canadian Institutes of Health Research; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; McGill University","keywords":"Microbead (research); Bacteria; Chromatography; Detection limit; Chemistry; Streptavidin; Point of care; Biotinylation; Microfluidics; Immunoassay; Nucleic acid methods; Conjugate; Nucleic acid; Nanotechnology; Antibody; Biotin; Biology; Biochemistry","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":[],"consensus_categories":[],"category_scores_codex":[0.00006233114,0.0001313632,0.000153674,0.00001364133,0.0002646577,0.0001031758,0.0001636493,0.0001296024,0.0001831352],"category_scores_gemma":[0.00003910842,0.0001383157,0.00006399218,0.00003170225,0.00008567161,0.00005468397,0.00002697048,0.0001027981,0.00001655175],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004299555,"about_ca_system_score_gemma":0.00001296718,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005959167,"about_ca_topic_score_gemma":3.267852e-7,"domain_scores_codex":[0.9993545,0.000002555504,0.0001471572,0.0002051195,0.00006004208,0.0002306598],"domain_scores_gemma":[0.999413,0.000024482,0.00002778521,0.0003924044,0.00003292935,0.000109428],"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":[0.000004077271,0.000007022395,0.00005414808,0.00009377747,0.00004037577,7.599408e-7,0.000009272233,1.044086e-7,0.9752713,0.00006829506,0.02073679,0.003714047],"study_design_scores_gemma":[0.0002179048,0.00001106355,0.0009067674,0.000006511538,0.00005346224,0.00001689167,0.00001223899,0.0006744032,0.8191018,0.00031738,0.1785089,0.0001726769],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9530187,0.03153704,0.00797914,0.000114755,0.00006365682,0.0001918571,0.00005504068,0.0001512843,0.006888542],"genre_scores_gemma":[0.9658111,0.03337163,0.00001200744,0.00002083669,0.0001341316,0.00004499709,0.00002680356,0.00002299837,0.0005555329],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1577721,"threshold_uncertainty_score":0.5640351,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01305934283059273,"score_gpt":0.2185343852144259,"score_spread":0.2054750423838332,"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."}}