{"id":"W2057683060","doi":"10.1177/0954410011417671","title":"Micro-electromechanical systems gyro performance improvement through bias correction over temperature using an adaptive neural network-trained fuzzy inference system","year":2011,"lang":"en","type":"article","venue":"Proceedings of the Institution of Mechanical Engineers Part G Journal of Aerospace Engineering","topic":"Fuzzy Logic and Control Systems","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Adaptive neuro fuzzy inference system; Fuzzy logic; Control theory (sociology); Artificial neural network; Compensation (psychology); Computer science; Controller (irrigation); Fuzzy control system; Artificial intelligence; Control (management)","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001103828,0.0003384666,0.0006780148,0.0001249132,0.0001315785,0.00007702978,0.001032182,0.0002328976,6.361212e-7],"category_scores_gemma":[0.0001819693,0.0002490526,0.0002521134,0.0006894623,0.00004916498,0.001453921,0.0001620064,0.0006373908,3.405762e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003064018,"about_ca_system_score_gemma":0.0001363139,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004066134,"about_ca_topic_score_gemma":7.50708e-7,"domain_scores_codex":[0.9974915,0.00002873578,0.00104491,0.0002951892,0.000685594,0.0004540602],"domain_scores_gemma":[0.9977776,0.00006129635,0.001069391,0.0002331077,0.0006951159,0.0001634364],"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":[0.0001903533,0.0001132514,0.00006594439,0.0003526348,0.0001776928,0.000003757156,0.0005057145,0.4843531,0.3591563,0.1548791,0.00005654629,0.0001455892],"study_design_scores_gemma":[0.0009326644,0.001596874,0.0001091088,0.001548725,0.00009284173,0.0002965345,0.0003597176,0.8834754,0.1111571,0.00009259002,0.00002486285,0.0003136207],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8338718,0.0003114173,0.158943,0.00003579787,0.00609515,0.0004801099,0.000003054728,0.0001095011,0.0001501481],"genre_scores_gemma":[0.9879616,0.00003143634,0.01156195,0.00001280065,0.0003898125,0.00001092244,2.963311e-7,0.0000204823,0.00001069293],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3991223,"threshold_uncertainty_score":0.9999962,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02931440450802622,"score_gpt":0.2088841360238833,"score_spread":0.1795697315158571,"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."}}