{"id":"W2111420168","doi":"10.1139/t06-077","title":"Modeling the mechanical behavior of railway ballast using artificial neural networks","year":2006,"lang":"en","type":"article","venue":"Canadian Geotechnical Journal","topic":"Railway Engineering and Dynamics","field":"Engineering","cited_by":47,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Ballast; Geotechnical engineering; Artificial neural network; Constitutive equation; Structural engineering; Engineering; Hardening (computing); Strain hardening exponent; Deformation (meteorology); Finite element method; Geology; Materials science; Computer science; Composite material","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003034416,0.0001822766,0.0002188528,0.0001573786,0.0001839041,0.0000852177,0.000330809,0.0002283926,0.00002914333],"category_scores_gemma":[0.00002736758,0.0001538208,0.0001611767,0.0002468129,0.00005158801,0.00007985453,0.00002014792,0.0009132159,0.000001794967],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002522854,"about_ca_system_score_gemma":0.00009842666,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004083178,"about_ca_topic_score_gemma":0.006005263,"domain_scores_codex":[0.9985919,0.00002533552,0.0005018276,0.0001207749,0.0002022801,0.0005578992],"domain_scores_gemma":[0.9993061,0.00003357854,0.00003680438,0.0002243455,0.00006713983,0.0003320624],"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":[0.000002502057,0.000006903905,0.0000166303,0.000003325038,0.00001001275,0.0000475458,0.000003493378,0.99542,0.001159558,0.001275885,0.00007327599,0.001980853],"study_design_scores_gemma":[0.00008901596,0.00001769998,0.0001043122,0.00002111486,0.00004218734,0.0004240643,0.00001440554,0.9985384,0.00005399754,0.0003932581,0.0001305748,0.0001709289],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3688434,0.0002579345,0.6298724,0.00008963756,0.0006718552,0.00009593816,0.0000162845,0.00009331021,0.00005924073],"genre_scores_gemma":[0.9981063,0.00001234883,0.001150486,0.0000292364,0.0006379053,0.000003881646,0.000005250763,0.00004940824,0.00000516119],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6292629,"threshold_uncertainty_score":0.6272629,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01274782890872336,"score_gpt":0.201148480531505,"score_spread":0.1884006516227817,"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."}}