{"id":"W4394687554","doi":"10.1016/j.biosx.2024.100479","title":"Sensitivity studies and optimization of an impedance-based biosensor for point-of-care applications","year":2024,"lang":"en","type":"article","venue":"Biosensors and Bioelectronics X","topic":"Analytical Chemistry and Sensors","field":"Chemical Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"China Scholarship Council","keywords":"Sensitivity (control systems); Biosensor; Point of care; Electrical impedance; Computer science; Point (geometry); Point-of-care testing; Materials science; Biochemical engineering; Biological system; Nanotechnology; Electronic engineering; Medicine; Biology; Mathematics; Engineering; Electrical engineering; Pathology","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":[0.000113041,0.000141682,0.0002553446,0.00005447883,0.0000491611,0.00001396082,0.00003297475,0.0001060412,0.000001859771],"category_scores_gemma":[0.00007818558,0.0001201947,0.00007209378,0.0001611079,0.0001566144,0.00004932803,0.00001641953,0.00008741572,2.223163e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003097903,"about_ca_system_score_gemma":0.00003264371,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000412119,"about_ca_topic_score_gemma":0.000003893439,"domain_scores_codex":[0.9992302,0.00001546594,0.0002205481,0.0002698303,0.00007995094,0.0001839832],"domain_scores_gemma":[0.999329,0.0002903443,0.0000480164,0.0001309133,0.0001371929,0.00006455354],"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":[0.0001706226,0.00006967112,0.0000371525,0.004542998,0.000225795,0.000003675986,0.000364538,0.01404638,0.9544419,0.0149679,0.00001853348,0.01111085],"study_design_scores_gemma":[0.0002045927,0.0001881069,0.00000504137,0.00008273842,0.0001006949,0.000004990431,0.0003680245,0.4590093,0.5393894,0.000164174,0.0003437452,0.0001391704],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9136444,0.01582495,0.0691363,0.0003874185,0.00003800429,0.0005089909,0.0002364811,0.0001486104,0.00007478395],"genre_scores_gemma":[0.9897056,0.0005198227,0.009591932,0.0000146636,0.00005775975,0.0000128057,0.00004096632,0.00001821268,0.00003824472],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4449629,"threshold_uncertainty_score":0.4901399,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01411114448375736,"score_gpt":0.2783713794884419,"score_spread":0.2642602350046845,"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."}}