{"id":"W2169173560","doi":"10.1109/mnrc.2008.4683409","title":"Optimization of DNA detection using FETs","year":2008,"lang":"en","type":"article","venue":"","topic":"Analytical Chemistry and Sensors","field":"Chemical Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"National Research Council Canada; CMC Microsystems","keywords":"Noise (video); Sensitivity (control systems); Biosensor; Transistor; Computer science; Electronic engineering; Optoelectronics; Field-effect transistor; CMOS; Electrode; Materials science; Nanotechnology; Electrical engineering; Engineering; Voltage; Physics; Artificial intelligence","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":[0.0006276611,0.0008781998,0.000670241,0.0003950344,0.0003271561,0.0007132769,0.0008221737,0.0007416572,0.001301521],"category_scores_gemma":[0.001551118,0.0004207342,0.0002721105,0.0004507573,0.0002582779,0.0007964502,0.0003359395,0.0004507086,0.0008783948],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005708416,"about_ca_system_score_gemma":0.000219183,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004956885,"about_ca_topic_score_gemma":0.001190141,"domain_scores_codex":[0.9994193,0.00008709444,0.00005743112,0.0001510087,0.0002125611,0.00007260771],"domain_scores_gemma":[0.9995657,0.0002147547,0.00005168939,0.00003091563,0.0001148541,0.00002210379],"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":[0.00005067476,0.00002292967,0.0001112466,0.00007969751,0.000006933381,0.00003963063,0.00001884985,0.000712153,0.9944478,0.0002741577,0.00008415664,0.00415191],"study_design_scores_gemma":[0.000007053166,0.00008815234,0.0002140627,0.000005555487,0.000007999221,0.00003962624,0.00001160612,0.00405715,0.9933493,0.00006369894,0.002148757,0.000006990427],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8591921,0.006494028,0.1233107,0.0006685834,0.0003487066,0.000334772,0.0006819678,0.001070117,0.007898963],"genre_scores_gemma":[0.8868804,0.003171933,0.1037362,0.000228038,0.00005451476,0.0003174344,0.0006358217,0.0002221028,0.004753445],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001301521,"threshold_uncertainty_score":0.00435406,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02297852919014934,"score_gpt":0.2228321571263404,"score_spread":0.199853627936191,"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."}}