{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041004,0.0005729325,0.000602796,0.0005090439,0.0003754413,0.0004182543,0.000491728,0.0006270069,0.001135708],"category_scores_gemma":[0.0009482006,0.0003816808,0.0005223147,0.0003517287,0.0002464541,0.0008433464,0.0004217411,0.0005796219,0.0003225204],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002568252,"about_ca_system_score_gemma":0.0004765804,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01363185,"about_ca_topic_score_gemma":0.00856292,"domain_scores_codex":[0.9996744,0.00003906166,0.00002182168,0.0001157819,0.000112181,0.00003682411],"domain_scores_gemma":[0.9996077,0.0001011141,0.00006656485,0.00004024834,0.0001684814,0.00001581829],"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":[0.0003743415,0.0001225929,0.008499148,0.0002301753,0.0001494489,0.000207864,0.0002433848,0.6853268,0.08519672,0.002054688,0.001036586,0.2165582],"study_design_scores_gemma":[0.0000091521,0.00005769777,0.001552774,0.000006949415,0.0000141714,0.00002530304,0.00001210503,0.9947221,0.003036501,0.0001957753,0.0003532966,0.00001411058],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08685705,0.0001906244,0.9108378,0.00007444909,0.00003727533,0.00003234416,0.00006108663,0.0005525397,0.001356782],"genre_scores_gemma":[0.9353649,0.0002160277,0.06182506,0.00003479823,0.00002269181,0.00005741101,0.000118653,0.00002205151,0.002338313],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01363185,"threshold_uncertainty_score":0.02710503,"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."}}