{"id":"W2895155795","doi":"10.1109/lsens.2018.2874062","title":"Topology: A Source of Nonlinearity in the MEMS Thermal Accelerometer","year":2018,"lang":"en","type":"article","venue":"IEEE Sensors Letters","topic":"Advanced MEMS and NEMS Technologies","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Canadian Institutes of Health Research; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Linearity; Accelerometer; Acceleration; Rotation (mathematics); Physics; Concentric; Nonlinear system; Symmetry (geometry); Topology (electrical circuits); Thermal; Gravitational acceleration; Proof mass; Natural convection; Microelectromechanical systems; Gravitation; Mechanics; Optics; Computational physics; Convection; Geometry; Electrical engineering; Classical mechanics; Engineering; Optoelectronics; Mathematics; Thermodynamics","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":[0.0003092292,0.0003432108,0.0002608745,0.0002007942,0.000263161,0.0003562168,0.0003345352,0.0004215072,0.0009533319],"category_scores_gemma":[0.00197817,0.0003187189,0.00017249,0.0002134487,0.000451399,0.0008051271,0.0003177195,0.000380933,0.0003800392],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002713964,"about_ca_system_score_gemma":0.0001262718,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001924762,"about_ca_topic_score_gemma":0.0004115491,"domain_scores_codex":[0.9995766,0.0001117913,0.00001395091,0.0000656481,0.0001947942,0.00003717954],"domain_scores_gemma":[0.9987617,0.0006300302,0.0002202091,0.0001600919,0.0001901635,0.00003785149],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001486826,0.00002448706,0.003501021,0.0001076685,0.00001453184,0.000478042,0.0003658748,0.009685186,0.9722617,0.001073173,0.000198943,0.0121407],"study_design_scores_gemma":[0.00001354077,0.0007841593,0.02848088,0.00002729432,0.00005202905,0.003141482,0.0002511565,0.08559334,0.8761105,0.001195678,0.004293311,0.00005660862],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9474193,0.0004238234,0.04736578,0.0002557005,0.00006925961,0.00002076909,0.00007884287,0.0004311916,0.003935463],"genre_scores_gemma":[0.9943433,0.0001323198,0.00445159,0.00002809637,0.00001780866,0.00001291723,0.00003988062,0.00006205172,0.0009119725],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0009533319,"threshold_uncertainty_score":0.003189266,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01623945694499508,"score_gpt":0.2395247525554343,"score_spread":0.2232852956104392,"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."}}