{"id":"W4361208674","doi":"10.1016/j.jterra.2023.03.002","title":"Terramechanics models augmented by machine learning representations","year":2023,"lang":"en","type":"article","venue":"Journal of Terramechanics","topic":"Soil Mechanics and Vehicle Dynamics","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":false,"ca_institutions":"CM Labs Simulations (Canada); McGill University","funders":"","keywords":"Component (thermodynamics); Discretization; Finite element method; Computer science; Artificial neural network; Field (mathematics); Empirical modelling; Mathematical optimization; Applied mathematics; Machine learning; Engineering; Simulation; Mathematics; Structural engineering","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":[0.0002872287,0.000681139,0.0008946467,0.0006507045,0.0002980181,0.00116072,0.00128659,0.001817045,0.004235109],"category_scores_gemma":[0.001555004,0.000628604,0.0008084679,0.0008687033,0.0005191735,0.001595498,0.0008333677,0.001628862,0.001527583],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000399567,"about_ca_system_score_gemma":0.000686055,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01203191,"about_ca_topic_score_gemma":0.01171453,"domain_scores_codex":[0.9998498,0.00003437921,0.000009031626,0.00004763608,0.00003481904,0.00002433127],"domain_scores_gemma":[0.9995477,0.0001895343,0.00006475079,0.00008717917,0.00008703819,0.00002389488],"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":[0.00002779169,0.00002690356,0.0002966853,0.00001802173,0.000021343,0.00003265455,0.000008903764,0.9768692,0.0005915956,0.002323069,0.0005851362,0.0191988],"study_design_scores_gemma":[0.000001707169,0.000003735716,0.00003919784,0.000001740272,0.000001925544,0.000003007993,0.000001263577,0.9989895,0.00006838031,0.0007328273,0.0001551158,0.000001650204],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09259888,0.000908459,0.8943799,0.0009135706,0.0003860264,0.00005948213,0.001165027,0.00239066,0.007198021],"genre_scores_gemma":[0.930529,0.0005605126,0.05981362,0.0002301985,0.0001478371,0.0000951273,0.001568531,0.0001316324,0.006923576],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01203191,"threshold_uncertainty_score":0.02392375,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01664927662205346,"score_gpt":0.2331648055945091,"score_spread":0.2165155289724556,"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."}}