{"id":"W2743449340","doi":"10.1139/cgj-2016-0634","title":"Design of ballasted railway track foundations using numerical modelling. Part II: Applications","year":2017,"lang":"en","type":"article","venue":"Canadian Geotechnical Journal","topic":"Railway Engineering and Dynamics","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Tonnage; Track (disk drive); Field (mathematics); Relation (database); Design methods; Computer science; Engineering; Reliability engineering; Structural engineering; Mechanical engineering; Data mining; Geology; Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009336513,0.0005154921,0.0005681198,0.0004918939,0.0004233009,0.0009512971,0.001038631,0.001081396,0.003140144],"category_scores_gemma":[0.001617716,0.0005328626,0.0005940135,0.0004416018,0.0005250608,0.000806339,0.0006790934,0.000627729,0.0007335998],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007475064,"about_ca_system_score_gemma":0.001028765,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002816139,"about_ca_topic_score_gemma":0.003842816,"domain_scores_codex":[0.9993435,0.0001700514,0.00005002368,0.00008360136,0.0003202959,0.00003244207],"domain_scores_gemma":[0.9994844,0.0001546885,0.00007493383,0.0001036961,0.0001563483,0.00002594064],"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":[0.00006316163,0.00005557443,0.0007696326,0.0004848921,0.00002358017,0.0001554586,0.0001802134,0.8422399,0.04241652,0.01238968,0.0006799087,0.1005414],"study_design_scores_gemma":[0.00002091537,0.0001346547,0.000352976,0.00005997507,0.00001553464,0.0001599619,0.0000639965,0.9721854,0.01219393,0.002262039,0.01252712,0.00002347728],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009347785,0.0001340512,0.9875731,0.00004902473,0.00002589193,0.00005208716,0.00002476523,0.0001679947,0.002625359],"genre_scores_gemma":[0.3937856,0.0005620098,0.5987399,0.00002646775,0.00001282094,0.0001648248,0.0000877243,0.00007846439,0.006542236],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003140144,"threshold_uncertainty_score":0.01050484,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03581314805877574,"score_gpt":0.2407560664715513,"score_spread":0.2049429184127756,"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."}}