{"id":"W3169542805","doi":"10.1186/s10033-021-00559-2","title":"Vertical Tire Forces Estimation of Multi-Axle Trucks Based on an Adaptive Treble Extend Kalman Filter","year":2021,"lang":"en","type":"article","venue":"Chinese Journal of Mechanical Engineering","topic":"Vehicle Dynamics and Control Systems","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Basic and Applied Basic Research Foundation of Guangdong Province; Science and Technology Planning Project of Guangdong Province","keywords":"Axle; Kalman filter; Truck; Control theory (sociology); Constraint (computer-aided design); Computer science; Process (computing); Filter (signal processing); Engineering; Automotive engineering; Control (management); Structural engineering; Mechanical engineering; Artificial intelligence","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":[0.0002693941,0.0002029318,0.0004513756,0.0001241733,0.00002365976,0.00002773997,0.0001632695,0.0001153827,0.00003336],"category_scores_gemma":[0.0001841917,0.0001618579,0.0001959587,0.0001806554,0.000007598868,0.0001913504,0.0000170998,0.000314044,0.000001836988],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007981911,"about_ca_system_score_gemma":0.00003276291,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002902001,"about_ca_topic_score_gemma":0.000005832022,"domain_scores_codex":[0.9987867,0.00003286068,0.000553893,0.0001263986,0.0002975259,0.0002026311],"domain_scores_gemma":[0.9993038,0.0001341161,0.00006692326,0.0002000412,0.0001202824,0.0001748342],"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":[0.00005386774,0.00008647397,0.00002383481,0.00007142789,0.00006266835,0.00005335902,0.00005003163,0.9477144,0.04529596,0.0005676246,0.000004880392,0.00601547],"study_design_scores_gemma":[0.001206548,0.0002957912,0.000854648,0.0002179394,0.00003425557,0.00005319311,0.00002538135,0.9921065,0.004969497,0.00006507669,0.00001429073,0.0001568658],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5890519,0.0001847927,0.4101264,0.00003125801,0.0004292239,0.00006250377,0.000007981374,0.00004587604,0.00006012642],"genre_scores_gemma":[0.9930344,0.000007551222,0.006733702,0.00001599842,0.0001468924,0.000003747095,0.000004869259,0.0000428141,0.000009988065],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4039825,"threshold_uncertainty_score":0.6600372,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007903725517876464,"score_gpt":0.221538455337823,"score_spread":0.2136347298199466,"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."}}