{"id":"W2607966658","doi":"10.1101/128538","title":"Amplified visual immunosensor integrated with nanozyme for ultrasensitive detection of avian influenza virus","year":2017,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Advanced Nanomaterials in Catalysis","field":"Materials Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Ministry of Agriculture, Food and Rural Affairs; Natural Sciences and Engineering Research Council of Canada; Ontario Ministry of Agriculture, Food and Rural Affairs","keywords":"Detection limit; Avian influenza virus; Colloidal gold; Immunoassay; Chemistry; Influenza A virus subtype H5N1; Bioanalysis; Nanotechnology; Combinatorial chemistry; Virus; Nanoparticle; Materials science; Chromatography; Virology; Antibody; Biology","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":[0.0002655906,0.0004954875,0.000261935,0.0002404039,0.0001008208,0.0003107856,0.0004362884,0.0005025857,0.0006916944],"category_scores_gemma":[0.0002482978,0.0002936546,0.00028895,0.0001415275,0.0001657029,0.0002568964,0.000247942,0.000309228,0.0004005925],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003190046,"about_ca_system_score_gemma":0.0001864299,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003667334,"about_ca_topic_score_gemma":0.0005712245,"domain_scores_codex":[0.9996924,0.00003481582,0.00002138817,0.00008772501,0.0001313935,0.00003230166],"domain_scores_gemma":[0.9999115,0.00002152916,0.0000223768,0.000007684869,0.00002559593,0.00001114142],"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":[0.00001710424,0.000009066806,0.00009896934,0.00002243106,0.000002553365,0.00002341068,0.000008496269,0.00007064816,0.9982858,0.00006144918,0.00002964737,0.001370448],"study_design_scores_gemma":[0.00000400717,0.00004861823,0.0004582771,0.00000187493,0.000005646167,0.00005815434,0.000005294849,0.002837651,0.9960293,0.00002344305,0.0005251784,0.00000256544],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8732379,0.00339209,0.1183236,0.0002505002,0.0002831379,0.00007206861,0.0001807761,0.0008884296,0.003371472],"genre_scores_gemma":[0.9551064,0.0005758586,0.04008447,0.00007783999,0.00002467309,0.0000389326,0.000167358,0.00002735053,0.003896975],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0006916944,"threshold_uncertainty_score":0.002314568,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01710510261329546,"score_gpt":0.2621318740324756,"score_spread":0.2450267714191801,"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."}}