{"id":"W2403795476","doi":"10.1016/j.sbsr.2016.05.005","title":"Optimized design of micromachined electric field mills to maximize electrostatic field sensitivity","year":2016,"lang":"en","type":"article","venue":"Sensing and Bio-Sensing Research","topic":"Advanced MEMS and NEMS Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Shutter; Electric field; Electrode; SIGNAL (programming language); Sense (electronics); Perforation; Sensitivity (control systems); Surface micromachining; Parametric statistics; Materials science; Acoustics; Shielded cable; Electrical engineering; Field (mathematics); Optics; Optoelectronics; Computer science; Electronic engineering; Engineering; Physics; Fabrication; Mathematics; Composite material","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":[0.0006038605,0.0008301764,0.0004804948,0.0003912933,0.000277757,0.001004759,0.0006856403,0.0006488715,0.001118833],"category_scores_gemma":[0.001251313,0.0005777717,0.0002298054,0.0002409457,0.0003754513,0.0007115469,0.0003721629,0.0004265913,0.0002583821],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006741057,"about_ca_system_score_gemma":0.0007467417,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002254105,"about_ca_topic_score_gemma":0.0006631868,"domain_scores_codex":[0.9996364,0.00003753021,0.00002790339,0.0001044218,0.0001475614,0.00004623578],"domain_scores_gemma":[0.9992216,0.0002398034,0.0002652954,0.00005762877,0.0001764613,0.0000393461],"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":[0.0002226401,0.0001368595,0.002016878,0.00048612,0.00005296085,0.0002502269,0.0001238403,0.2062289,0.7524858,0.004979341,0.0004760285,0.03254043],"study_design_scores_gemma":[0.0001705944,0.001456406,0.003544698,0.00005588101,0.00006858886,0.0004281051,0.000169622,0.4260938,0.5560713,0.00265974,0.009210138,0.00007117469],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5618802,0.001212599,0.4263255,0.0002446678,0.0001071361,0.000344221,0.0002055939,0.0005956089,0.009084564],"genre_scores_gemma":[0.8374407,0.0002535804,0.160459,0.00003054678,0.00001434111,0.0001646254,0.00007141223,0.00004989244,0.001515821],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001118833,"threshold_uncertainty_score":0.004890978,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02843907925170659,"score_gpt":0.2986419160158951,"score_spread":0.2702028367641885,"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."}}