{"id":"W4388934043","doi":"10.1088/1361-665x/ad0f36","title":"Sequential neural network model for the identification of magnetorheological damper parameters","year":2023,"lang":"en","type":"article","venue":"Smart Materials and Structures","topic":"Vibration Control and Rheological Fluids","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Parametric statistics; Artificial neural network; Magnetorheological damper; Damper; Parametric model; Robustness (evolution); Control theory (sociology); Nonlinear system; Computer science; Generalization; Magnetorheological fluid; Engineering; Artificial intelligence; Mathematics; Structural engineering; Physics; Mathematical analysis","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":[0.0002571196,0.00008193633,0.0001515763,0.00001553614,0.00008739325,0.00005051579,0.00008075978,0.00007410972,0.00004477112],"category_scores_gemma":[0.00003329284,0.00004616589,0.00004106804,0.00004837233,0.00007832811,0.00004122105,0.0000251897,0.00003102485,0.000001417226],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000002168381,"about_ca_system_score_gemma":0.000003102388,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003983622,"about_ca_topic_score_gemma":0.000002916512,"domain_scores_codex":[0.9993764,0.00003237419,0.0002894806,0.0001033457,0.00005641174,0.0001420167],"domain_scores_gemma":[0.9997376,0.00009429755,0.00003161127,0.00009506622,0.00001918874,0.00002219229],"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":[0.00006582256,0.000001841108,0.0002971043,0.00005208541,0.00004999639,4.590278e-7,0.00007535377,0.6913868,0.2835696,0.01869796,0.003777744,0.002025224],"study_design_scores_gemma":[0.0002034206,0.00003042956,0.02710046,0.000001850967,0.00002660447,0.000001479324,0.000009117458,0.9449213,0.007977311,0.01950115,0.0001480506,0.00007884467],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9771919,0.0001997455,0.02073878,0.0001301049,0.001325262,0.0002256026,0.00007179481,0.0001039819,0.00001278862],"genre_scores_gemma":[0.9990487,0.0001213886,0.0004264437,0.00005324991,0.0001902467,0.00003576117,0.00003533898,0.000007717344,0.0000812291],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2755923,"threshold_uncertainty_score":0.188259,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02381528858943159,"score_gpt":0.2370391106130043,"score_spread":0.2132238220235728,"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."}}