{"id":"W4393106994","doi":"10.1007/10_2024_251","title":"Trends in Development of Aptamer-Based Biosensor Technology for Detection of Bacteria","year":2024,"lang":"en","type":"article","venue":"Advances in biochemical engineering, biotechnology","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Aptamer; Biosensor; Food safety; Nanotechnology; Biochemical engineering; Foodborne pathogen; Contamination; Biotechnology; Computer science; Engineering; Biology; Bacteria; Materials science; Food science; Molecular biology; Ecology","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":[0.0001348561,0.0002084794,0.0003334323,0.001258448,0.00001045844,0.000003020766,0.0002042354,0.0006788409,8.36724e-7],"category_scores_gemma":[0.0001424311,0.0002020777,0.0001012542,0.001165986,0.0002140435,0.00000673388,0.00006884807,0.0001831662,2.333328e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004983917,"about_ca_system_score_gemma":0.0000356411,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001776693,"about_ca_topic_score_gemma":0.00002263899,"domain_scores_codex":[0.9986616,0.000007624966,0.0005220045,0.0004586323,0.00007058495,0.0002795085],"domain_scores_gemma":[0.9995235,0.00002427908,0.00009571874,0.0002883289,0.00004558335,0.00002257542],"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":[0.00006130221,0.00006032513,0.00004931501,0.0001392756,0.00002022214,0.000001563271,0.000004576808,0.0001173618,0.9191408,0.0002347402,0.00000367673,0.08016688],"study_design_scores_gemma":[0.0002813636,0.0002286206,0.00004979399,0.0001433063,0.000009683329,0.00000576872,0.00001548547,0.002095412,0.9759923,0.00007829774,0.02089597,0.0002040662],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9275047,0.004954852,0.06690484,0.0001593356,0.0001173059,0.0001675389,0.0000278455,0.0001482477,0.00001538983],"genre_scores_gemma":[0.9174861,0.0003125837,0.08203149,0.000006466575,0.0000209867,0.00004856648,0.00006090541,0.00002382229,0.000009141287],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07996282,"threshold_uncertainty_score":0.8240489,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004660515397186943,"score_gpt":0.2594590968165146,"score_spread":0.2547985814193277,"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."}}