{"id":"W4382516693","doi":"10.4203/ccc.1.27.19","title":"Railway Track Substructure Evaluation Using Instrumented Wheelset Continuous Measurements","year":2023,"lang":"en","type":"article","venue":"Civil-comp conferences","topic":"Railway Engineering and Dynamics","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"National Research Council Canada; Natural Sciences and Engineering Research Council of Canada; Transport Canada","keywords":"Substructure; Track (disk drive); Computer science; Structural engineering; Automotive engineering; Engineering; Operating system","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004484205,0.0002985291,0.0003790992,0.0001835339,0.00009818807,0.0001283472,0.0002400878,0.0001417441,0.000249097],"category_scores_gemma":[0.0000539652,0.0002804346,0.0001118344,0.0003876947,0.00004493651,0.0001803427,0.00002506898,0.0002078042,0.00005063538],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009873109,"about_ca_system_score_gemma":0.00007062241,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004676783,"about_ca_topic_score_gemma":0.0001673726,"domain_scores_codex":[0.9983708,0.00005563325,0.0003331988,0.0002720718,0.000552604,0.0004156879],"domain_scores_gemma":[0.9993722,0.00004403016,0.00005413527,0.0002643633,0.0001608486,0.0001043553],"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":[0.00001213103,0.00002449777,0.00559545,0.0001972278,0.001142904,0.000009756214,0.001090436,0.9490856,0.01750642,0.0003836145,0.001959255,0.02299275],"study_design_scores_gemma":[0.0005773496,0.00002346319,0.008120401,0.00005688591,0.0002891016,0.000008226968,0.0001998681,0.9864125,0.001583432,0.0004850005,0.001857262,0.0003865223],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9837173,0.0006341175,0.004524409,0.00003126021,0.001685097,0.000391002,0.00008381482,0.001347167,0.007585876],"genre_scores_gemma":[0.9991164,0.00007149666,0.0003689525,0.00001127353,0.00009927128,0.00003951287,0.0001829553,0.00004342376,0.00006673892],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03732692,"threshold_uncertainty_score":0.9999648,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05826500358677663,"score_gpt":0.265276039674117,"score_spread":0.2070110360873404,"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."}}