{"id":"W2754557803","doi":"10.1016/j.proeng.2017.09.482","title":"Estimation of track modulus over long distances using artificial neural networks","year":2017,"lang":"en","type":"article","venue":"Procedia Engineering","topic":"Railway Engineering and Dynamics","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Artificial neural network; Track (disk drive); Estimation; Computer science; Artificial intelligence; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00009694048,0.0002258029,0.000247191,0.0000935756,0.00009460738,0.0001112014,0.00023992,0.0001073504,0.000004722163],"category_scores_gemma":[0.00009779288,0.0002533327,0.00007273812,0.0000943336,0.00003109182,0.0004373492,0.00003107451,0.0002094166,0.000001193696],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006359258,"about_ca_system_score_gemma":0.00000820115,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000959418,"about_ca_topic_score_gemma":0.00000672417,"domain_scores_codex":[0.9990304,0.000002344807,0.000302562,0.0001674674,0.0001616827,0.0003356177],"domain_scores_gemma":[0.9994552,0.00002665434,0.00007163531,0.0003330941,0.0000278934,0.00008558959],"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":[0.00000418718,0.000007319175,0.0005480211,0.0002401048,0.00002345662,0.000004663437,0.00006342307,0.9840536,0.001413154,0.0004235282,0.000004889616,0.0132137],"study_design_scores_gemma":[0.0001183993,0.0000104453,0.008366712,0.00008505373,0.00002282755,0.000007856564,0.000005128603,0.9893106,0.001757899,0.00005046365,0.000008911066,0.0002556952],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5192388,0.0002887833,0.4791484,0.000003163458,0.0009022666,0.00007582922,0.000007051199,0.0002635224,0.00007226467],"genre_scores_gemma":[0.9941664,0.00002465202,0.005421196,0.000001317208,0.0002893396,0.00001092853,0.000009573626,0.00007018918,0.000006357373],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4749277,"threshold_uncertainty_score":0.9999919,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01086279728050134,"score_gpt":0.223613317097916,"score_spread":0.2127505198174146,"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."}}