{"id":"W4412595850","doi":"10.3390/app15158181","title":"Multi-Objective Automated Machine Learning for Inversion of Mesoscopic Parameters in Discrete Element Contact Models","year":2025,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Manufacturing Process and Optimization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"Centre Scientifique et Technique du Bâtiment; Postdoctoral Research Foundation of China; National Natural Science Foundation of China","keywords":"Mesoscopic physics; Inversion (geology); Computer science; Artificial intelligence; Machine learning; Geology; 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":[0.0009312229,0.0009256543,0.0006830244,0.0005483392,0.0002948331,0.0004711353,0.000891568,0.0007464531,0.001157055],"category_scores_gemma":[0.002015699,0.0004534927,0.0006727112,0.0003659059,0.0004235594,0.000509667,0.0006966348,0.001112542,0.000253897],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004781821,"about_ca_system_score_gemma":0.0008844588,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005463844,"about_ca_topic_score_gemma":0.005398178,"domain_scores_codex":[0.9997674,0.00007105208,0.00001437214,0.00005270353,0.00006464309,0.00002990544],"domain_scores_gemma":[0.999089,0.0005660998,0.000105163,0.00006303302,0.0001440701,0.00003261874],"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":[0.00001955694,0.00003042704,0.0006450856,0.00003178595,0.00001416131,0.00001724196,0.00002862229,0.9730584,0.001536925,0.0004800053,0.0001267439,0.02401102],"study_design_scores_gemma":[7.822448e-7,0.000004064621,0.00003995533,9.307334e-7,5.139018e-7,9.742271e-7,0.000001352178,0.9996301,0.0001903875,0.0001017751,0.00002827912,8.506935e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08890168,0.0002338705,0.9080853,0.0001025373,0.00002209313,0.00006982652,0.00007542592,0.001137235,0.001371953],"genre_scores_gemma":[0.8552761,0.00007937152,0.1429175,0.00007042423,0.00001547655,0.0002502403,0.0001957097,0.00008302007,0.001112106],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005463844,"threshold_uncertainty_score":0.01086408,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01593629412381137,"score_gpt":0.2541634305865491,"score_spread":0.2382271364627377,"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."}}