{"id":"W4220667661","doi":"10.3390/mi13030425","title":"A Hybrid Microfluidic Electronic Sensing Platform for Life Science Applications","year":2022,"lang":"en","type":"article","venue":"Micromachines","topic":"Analytical Chemistry and Sensors","field":"Chemical Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Mitacs; CMC Microsystems","keywords":"JFET; Microfluidics; Sensitivity (control systems); Transconductance; Voltage; Materials science; Optoelectronics; Electronic engineering; Nanotechnology; Electrical engineering; Field-effect transistor; Engineering; Transistor","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":[0.0002094347,0.0004830951,0.0002624469,0.0003928576,0.0002172764,0.0003586634,0.0006087866,0.0005198291,0.0007415501],"category_scores_gemma":[0.0002354968,0.0001965573,0.0003037894,0.0002519082,0.0002253725,0.0006001295,0.0004115996,0.0002359205,0.0003458439],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002798639,"about_ca_system_score_gemma":0.0003207579,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001889995,"about_ca_topic_score_gemma":0.0003606315,"domain_scores_codex":[0.9998111,0.00001034503,0.000009694089,0.00006062072,0.00008711155,0.00002117503],"domain_scores_gemma":[0.9999286,0.00001558515,0.00002248812,0.000009650259,0.00001579963,0.0000077779],"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":[0.00003859407,0.00002228096,0.0003822282,0.0001042508,0.00001566293,0.0001478256,0.00001518123,0.0006157287,0.9752684,0.001657304,0.0003787899,0.02135374],"study_design_scores_gemma":[0.00002350273,0.0004910376,0.002238339,0.00002343876,0.00005314541,0.000955773,0.00002011703,0.01552107,0.9575413,0.001011882,0.022076,0.00004426901],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4209918,0.01247337,0.5461825,0.0008068157,0.001190045,0.0003469556,0.001243084,0.002571063,0.01419442],"genre_scores_gemma":[0.7941609,0.003466473,0.1956704,0.0004241314,0.0002169973,0.0002077954,0.0003935757,0.00004080822,0.005418932],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0007415501,"threshold_uncertainty_score":0.002480686,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009826506196542377,"score_gpt":0.2397334056880424,"score_spread":0.2299068994915,"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."}}