{"id":"W3155595838","doi":"10.3390/bios11040117","title":"Detection of Sub-Nanomolar Concentration of Trypsin by Thickness-Shear Mode Acoustic Biosensor and Spectrophotometry","year":2021,"lang":"en","type":"article","venue":"Biosensors","topic":"Identification and Quantification in Food","field":"Biochemistry, Genetics and Molecular Biology","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Horizon 2020 Framework Programme; Vedecká Grantová Agentúra MŠVVaŠ SR a SAV; European Commission","keywords":"Casein; Biosensor; Detection limit; Chemistry; Trypsin; Colloidal gold; Protease; Chromatography; Substrate (aquarium); Analytical Chemistry (journal); Nanoparticle; Materials science; Enzyme; Nanotechnology; Biochemistry","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0001058123,0.0001012333,0.0001448785,0.00004692478,0.00005029382,0.00001555989,0.00005887146,0.0001511561,0.00001331701],"category_scores_gemma":[0.0001631716,0.0001084675,0.000050824,0.0002092389,0.0001428938,0.000005232343,0.00002187021,0.00005386772,0.000004281667],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009772226,"about_ca_system_score_gemma":0.00004963252,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001245929,"about_ca_topic_score_gemma":0.000007828886,"domain_scores_codex":[0.9990693,0.00008888355,0.0003077095,0.000284484,0.0001343555,0.0001153108],"domain_scores_gemma":[0.9992039,0.00001705295,0.0001835721,0.0002891968,0.0002597053,0.00004655839],"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":[0.00004502164,0.00007554498,0.0003400579,0.00005408807,0.0000313469,5.384383e-7,0.00003573323,0.00001283018,0.9988104,0.00009355354,0.0002233408,0.0002776108],"study_design_scores_gemma":[0.000317507,0.00008555491,0.003880237,0.0000104516,0.00002683584,0.0000181051,0.0001736151,0.0001689846,0.9937184,0.00001023997,0.001481961,0.0001081366],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9970685,0.0008785317,0.001491272,0.00007047554,0.0001601437,0.0001182604,0.0001023989,0.000009235532,0.0001012211],"genre_scores_gemma":[0.9984859,0.0006790945,0.0002820804,0.00003404932,0.00003669088,0.00000415306,0.0001517623,0.0000139265,0.0003123713],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005091961,"threshold_uncertainty_score":0.4423177,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009024513412031861,"score_gpt":0.2477822501649644,"score_spread":0.2387577367529325,"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."}}