{"id":"W2102953544","doi":"10.1504/ijhvs.2013.056912","title":"A parallel design optimisation method for articulated heavy vehicles with active safety systems","year":2013,"lang":"en","type":"article","venue":"International Journal of Heavy Vehicle Systems","topic":"Vehicle Dynamics and Control Systems","field":"Engineering","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Computation; Engineering; Simulation; Control engineering; Task (project management); Active safety; Active steering; Function (biology); Computer science; Automotive engineering; Systems engineering; Artificial intelligence; Control (management); Algorithm","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":[0.0009659879,0.0002908343,0.0005956102,0.0002570521,0.00009004172,0.0004113729,0.0004488709,0.0001603894,0.00001113737],"category_scores_gemma":[0.00004930954,0.0002298917,0.0001804063,0.0001511381,0.00003032159,0.0006617141,0.00002001817,0.0002582407,0.00002158441],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004892209,"about_ca_system_score_gemma":0.0000988547,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004234216,"about_ca_topic_score_gemma":0.000007610323,"domain_scores_codex":[0.9972387,0.0002454284,0.001182232,0.000215297,0.0007486523,0.0003697093],"domain_scores_gemma":[0.9970552,0.0004260461,0.0005403621,0.000200765,0.001575505,0.0002021434],"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":[0.0008762043,0.00006748953,0.0001617317,0.00008868347,0.001101308,0.0000263376,0.0003081686,0.9749126,0.01185767,0.00102315,0.0009735017,0.008603173],"study_design_scores_gemma":[0.002756871,0.0003369478,0.001001837,0.0004173143,0.00006173351,0.0005394565,0.0007049826,0.9910484,0.000808678,0.0001029571,0.001941671,0.0002791373],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1101305,0.001007874,0.8841019,0.0005022398,0.002280986,0.001557275,0.00004761643,0.0001147643,0.0002568366],"genre_scores_gemma":[0.9891248,0.00004930616,0.009632796,0.00003462929,0.0007080177,0.0002262473,0.00001324423,0.00007343431,0.0001374989],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8789943,"threshold_uncertainty_score":0.9374712,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01247439762635688,"score_gpt":0.2420585642583563,"score_spread":0.2295841666319994,"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."}}