{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000122984,0.0001724289,0.0001551572,0.00004703517,0.0001208335,0.00001867795,0.0001243386,0.00004412427,0.000082672],"category_scores_gemma":[0.00003807052,0.0001848492,0.00005209846,0.0001161197,0.00001509071,0.00004050087,0.0001198815,0.0002929499,0.0000124611],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008857585,"about_ca_system_score_gemma":0.000006241712,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007931219,"about_ca_topic_score_gemma":3.564667e-8,"domain_scores_codex":[0.9990343,0.0000122733,0.0002032299,0.0002794485,0.0001941584,0.0002765914],"domain_scores_gemma":[0.9996078,0.00006688701,0.00001793728,0.0001681823,0.000007891166,0.0001313121],"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":[0.00005717328,0.00002053825,7.016301e-7,0.000008729899,0.00001152051,0.00001463057,0.00009311486,0.6383877,0.3611827,0.0001130543,0.00007509845,0.00003503425],"study_design_scores_gemma":[0.0003207166,0.00004317231,3.753269e-7,0.000006550033,0.000005521856,0.000008417445,0.0001483398,0.700581,0.2981725,0.000004472802,0.0005457766,0.0001631457],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9123122,0.0003637692,0.08461097,0.0001233142,0.0002480242,0.0001506529,0.00005020255,0.0004624346,0.00167851],"genre_scores_gemma":[0.9969426,0.00000384347,0.002457866,0.00004119051,0.00008124326,0.00001839031,0.00001204063,0.00003277931,0.0004100234],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0846305,"threshold_uncertainty_score":0.7537932,"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."}}