{"id":"W4220780523","doi":"10.4271/2022-01-0884","title":"Development of a Prediction Model for Tire Tread Pattern Noise Based on Convolutional Neural Network with RMSProp Algorithm","year":2022,"lang":"en","type":"article","venue":"SAE technical papers on CD-ROM/SAE technical paper series","topic":"Engineering Applied Research","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Nexen (Canada)","funders":"","keywords":"Tread; Convolutional neural network; Computer science; Noise (video); Artificial intelligence; Algorithm; Artificial neural network; Pattern recognition (psychology); Image (mathematics); Materials science","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":[0.0005855047,0.0005902713,0.0006297349,0.0002261317,0.0004429343,0.00003256079,0.0006556651,0.0003170783,0.0001281392],"category_scores_gemma":[0.00006028545,0.000532147,0.000226461,0.0006360722,0.0003015084,0.000126898,0.0002143366,0.001269643,0.000007732687],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006979079,"about_ca_system_score_gemma":0.0001846248,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005147727,"about_ca_topic_score_gemma":0.002063729,"domain_scores_codex":[0.9962254,0.00005237199,0.0008031485,0.0007512665,0.001260146,0.0009077349],"domain_scores_gemma":[0.9984817,0.0003123598,0.0001017054,0.0007564046,0.00008958756,0.0002582344],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0004467218,0.0002993844,0.00005495359,0.0001427758,0.00006975279,0.00001279935,0.00003474055,0.5869018,0.3829636,0.0005342597,0.001953659,0.02658554],"study_design_scores_gemma":[0.004676125,0.004479267,0.8224179,0.0004956608,0.0001606579,0.0000855122,0.0001263693,0.1417751,0.0004532045,0.0003445489,0.02301278,0.001972815],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9183407,0.0008651447,0.0163834,0.003745712,0.001541624,0.01359054,0.004162994,0.02074696,0.02062297],"genre_scores_gemma":[0.9546187,0.00001387649,0.04164472,0.000238127,0.0001157019,0.002793666,0.0002778146,0.0001879698,0.0001094315],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.822363,"threshold_uncertainty_score":0.999713,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01318145642010946,"score_gpt":0.2267137088944647,"score_spread":0.2135322524743552,"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."}}