{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002768322,0.0006361903,0.0004231439,0.0004775581,0.0002929988,0.0003421093,0.001082562,0.000659421,0.00358059],"category_scores_gemma":[0.0005045417,0.0003603467,0.0003271701,0.0002985078,0.0002676206,0.0002983297,0.0004522251,0.0005675004,0.0008797157],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005174599,"about_ca_system_score_gemma":0.0005661481,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000712753,"about_ca_topic_score_gemma":0.0009444476,"domain_scores_codex":[0.999535,0.00002639056,0.00002909705,0.0001398729,0.0002207529,0.00004888553],"domain_scores_gemma":[0.999737,0.00007633711,0.00005337729,0.00002659579,0.00008008694,0.00002655681],"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":[0.00003514225,0.00002562026,0.0001311736,0.0001241169,0.00000950972,0.00006087634,0.00001568592,0.0003692701,0.9883602,0.0005084107,0.001237226,0.009122817],"study_design_scores_gemma":[0.00004826946,0.000313704,0.001703449,0.00001656167,0.00002446882,0.0003316071,0.000007630378,0.01577668,0.9640521,0.0001186146,0.01755752,0.0000493789],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3340788,0.005533929,0.6298192,0.0009124506,0.001218676,0.001540206,0.002431608,0.01192868,0.01253639],"genre_scores_gemma":[0.6835402,0.002159924,0.2973918,0.0008556422,0.0002670796,0.001991005,0.001600306,0.0002987332,0.01189528],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00358059,"threshold_uncertainty_score":0.01197833,"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."}}