{"id":"W4280622820","doi":"10.3390/bioengineering9050218","title":"Oral Cells-On-Chip: Design, Modeling and Experimental Results","year":2022,"lang":"en","type":"article","venue":"Bioengineering","topic":"Analytical Chemistry and Sensors","field":"Chemical Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Capacitive sensing; Capacitance; CMOS; Chip; Computer science; Microfabrication; Electronic engineering; Materials science; Nanotechnology; Electrical engineering; Electrode; Engineering; Chemistry; Medicine; Telecommunications","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":[0.0006945857,0.00101745,0.000776354,0.0003811195,0.0003712654,0.0008952036,0.001762925,0.001046661,0.005834003],"category_scores_gemma":[0.0008534689,0.0002974073,0.0006932159,0.0004433393,0.0003036443,0.0006522198,0.0003265955,0.0005037668,0.001600662],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001316867,"about_ca_system_score_gemma":0.001156833,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006350643,"about_ca_topic_score_gemma":0.004666263,"domain_scores_codex":[0.999245,0.0001327813,0.00002681676,0.0001164289,0.0003959348,0.00008302878],"domain_scores_gemma":[0.9994819,0.0001023348,0.00004964591,0.00005903976,0.0002795982,0.00002743347],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005201587,0.0004915167,0.00616967,0.002169392,0.0002225356,0.0005872049,0.0003653877,0.7773347,0.09657258,0.004833939,0.01178259,0.0989504],"study_design_scores_gemma":[0.0001037639,0.001871458,0.003404606,0.0000778476,0.0001552645,0.0001920949,0.0001350005,0.8738513,0.09754501,0.0007385481,0.02183425,0.00009086562],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.52366,0.007053758,0.3998394,0.001834901,0.0008716943,0.001633314,0.004203009,0.007729647,0.05317424],"genre_scores_gemma":[0.8760592,0.001870526,0.1020128,0.0002497393,0.00005174455,0.001207049,0.001326699,0.0002305396,0.01699165],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.006350643,"threshold_uncertainty_score":0.01951671,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03129717096132865,"score_gpt":0.2352604398342965,"score_spread":0.2039632688729679,"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."}}