{"id":"W4380303725","doi":"10.1109/tsmc.2023.3281474","title":"A Soft Sensor for Estimating Tire Cornering Properties for Intelligent Tires","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Systems Man and Cybernetics Systems","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"National Key Research and Development Program of China; Fundação de Amparo à Pesquisa do Estado de Minas Gerais; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; National Natural Science Foundation of China","keywords":"Accelerometer; Notation; Acceleration; Mathematics; Computer science; Artificial intelligence; Algorithm; Engineering; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002335327,0.0005583229,0.0006185651,0.0005443154,0.0002525096,0.000519416,0.0005662566,0.0007983655,0.001151376],"category_scores_gemma":[0.0009382658,0.0002472942,0.0003517391,0.0003943932,0.0002871734,0.0009688349,0.0005479194,0.0005813414,0.0006339116],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000265519,"about_ca_system_score_gemma":0.0004111293,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001241484,"about_ca_topic_score_gemma":0.001602436,"domain_scores_codex":[0.9996176,0.00003838104,0.00001962313,0.00009512871,0.0002005953,0.00002871325],"domain_scores_gemma":[0.9995642,0.0001073655,0.0000655809,0.00006156362,0.0001707972,0.00003058655],"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":[0.0003685125,0.0001432176,0.004770637,0.0003121357,0.0000692058,0.0002305908,0.0001576784,0.05431455,0.4892437,0.002992395,0.003405018,0.4439923],"study_design_scores_gemma":[0.0000232756,0.0002897344,0.007369807,0.00002912201,0.00004300724,0.0001954144,0.00005786336,0.8618378,0.1240785,0.001593056,0.004404645,0.00007777356],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07294808,0.0004310732,0.9223286,0.0001362723,0.0001492943,0.00006854751,0.0002196164,0.00141089,0.002307639],"genre_scores_gemma":[0.8414222,0.0002856659,0.154002,0.0001144348,0.00006813328,0.00007381016,0.0003351687,0.00004271657,0.003655927],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001241484,"threshold_uncertainty_score":0.003851771,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05480054578835583,"score_gpt":0.2805920665660529,"score_spread":0.2257915207776971,"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."}}