{"id":"W2467788496","doi":"10.1039/c6an00729e","title":"A “chemical nose” biosensor for detecting proteins in complex mixtures","year":2016,"lang":"en","type":"article","venue":"The Analyst","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"Regional Municipality of Waterloo; National Institute for Nanotechnology; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Chemistry; Bovine serum albumin; Lysozyme; Biosensor; Antibody; Chromatography; Immunoassay; Human serum albumin; Proteomics; Flow cytometry; Blood proteins; Detection limit; Colloidal gold; Biochemistry; Nanoparticle; Molecular biology; Nanotechnology; Biology; Immunology; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002435296,0.0001244347,0.0001559363,0.00005200702,0.00007292161,0.00001326528,0.0001864369,0.00009408828,0.000001583703],"category_scores_gemma":[0.0001787328,0.00006338823,0.0001479733,0.0001441174,0.0001138231,0.000002108007,0.00006395485,0.00004756256,0.000001588566],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001676669,"about_ca_system_score_gemma":0.00001294092,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002173424,"about_ca_topic_score_gemma":0.0001451331,"domain_scores_codex":[0.9991758,0.00005155055,0.0001943402,0.0002761729,0.00007845808,0.0002236574],"domain_scores_gemma":[0.999429,0.00004115282,0.00008445471,0.0003475796,0.00006609821,0.00003165322],"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":[0.00007833847,0.00002205816,0.0002783302,0.000004253371,0.00004215392,9.230574e-7,0.000007451432,8.798054e-7,0.9913998,0.00003184733,0.0003268337,0.007807131],"study_design_scores_gemma":[0.000290732,0.00006820845,0.0001765808,0.00001553477,0.00003116731,0.000007224276,0.00002714804,0.00008220963,0.9918373,0.0002512555,0.007081399,0.0001312861],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9809744,0.00008639527,0.01707345,0.001350907,0.00001186871,0.0002946191,0.00002070241,0.00003297794,0.000154711],"genre_scores_gemma":[0.9925274,0.00002360394,0.006773776,0.0002307369,0.0001759665,0.000029465,0.00002403919,0.00001385996,0.0002011384],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01155304,"threshold_uncertainty_score":0.2584897,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01990391293527505,"score_gpt":0.2922063043782475,"score_spread":0.2723023914429725,"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."}}