{"id":"W3025747650","doi":"10.1088/1361-6439/ab9203","title":"Frequency characteristics and thermal compensation of MEMS devices based on geometric anti-spring","year":2020,"lang":"en","type":"article","venue":"Journal of Micromechanics and Microengineering","topic":"Advanced MEMS and NEMS Technologies","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"National Key Research and Development Program of China; China Scholarship Council; National Natural Science Foundation of China","keywords":"Spring (device); Microelectromechanical systems; Compensation (psychology); Thermal; Materials science; Mechanical engineering; Electronic engineering; Engineering; Structural engineering; Acoustics; Optoelectronics; Physics","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0001012983,0.0001422361,0.0003038331,0.0002503516,0.00002398705,0.00002074842,0.00009802604,0.00006847565,0.000002854455],"category_scores_gemma":[0.00004194886,0.0001324885,0.00004274273,0.0002051153,0.00001325575,0.000105932,0.00002689294,0.0002308275,3.26594e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002017395,"about_ca_system_score_gemma":0.000007446422,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001330787,"about_ca_topic_score_gemma":2.759399e-7,"domain_scores_codex":[0.9993454,0.000002243146,0.0003359409,0.00008771695,0.00008659736,0.0001421551],"domain_scores_gemma":[0.9996263,0.00004336188,0.0001402713,0.00006871577,0.0000539645,0.00006735272],"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":[0.000005107775,0.00000770364,0.0002175905,0.0002069588,0.00002552358,0.0000110269,0.00004671083,0.004115471,0.9881271,0.00007366678,0.000003899004,0.007159257],"study_design_scores_gemma":[0.0006916727,0.000292077,0.009224997,0.0003644045,0.00004689943,0.000043859,0.00009175821,0.05549029,0.9329659,0.00003410101,0.0004893012,0.0002647324],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9643924,0.002017322,0.0332658,0.00006779125,0.000138304,0.00005370145,0.000009119368,0.00004823178,0.000007311784],"genre_scores_gemma":[0.98952,0.001277763,0.009094208,0.00003334234,0.00004757717,5.305589e-7,7.716503e-7,0.00002545012,3.401531e-7],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05516117,"threshold_uncertainty_score":0.5402723,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01000632906502381,"score_gpt":0.1889237366245911,"score_spread":0.1789174075595673,"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."}}