{"id":"W2963984670","doi":"10.1115/jrc2019-1284","title":"Estimating the Outer Ring Defect Size and Remaining Service Life of Freight Railcar Bearings Using Vibration Signatures","year":2019,"lang":"en","type":"article","venue":"","topic":"Railway Engineering and Dynamics","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Bearing (navigation); Spall; Engineering; Automotive engineering; Schedule; Vibration; Raceway; Detector; Turbofan; Condition monitoring; Truck; Computer science; Structural engineering; Electrical engineering; Lubrication; Mechanical engineering; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002057881,0.0001498803,0.0001780462,0.00004953441,0.00005583213,0.00005748799,0.0001040499,0.00008648045,0.00001930125],"category_scores_gemma":[0.00008038885,0.0001168997,0.00004508935,0.0001610748,0.00001133744,0.0001889333,0.0000495716,0.0001953293,0.000003897389],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000215231,"about_ca_system_score_gemma":0.00001047361,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004948779,"about_ca_topic_score_gemma":0.000008557919,"domain_scores_codex":[0.9993175,0.00001318515,0.000214499,0.0001418018,0.0001276632,0.000185322],"domain_scores_gemma":[0.9994312,0.0002450981,0.00004075664,0.0002019945,0.00003426481,0.00004670368],"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":[0.00000224105,0.000001612919,0.001112684,0.0001945846,0.0000370048,5.069834e-7,0.0005748713,0.9490163,0.04852876,0.0003372263,0.000004480748,0.0001897506],"study_design_scores_gemma":[0.0001721155,0.00001038918,0.003851369,0.0001118877,0.00002232298,0.000005911448,0.00009707164,0.9939558,0.001540756,0.00006930846,0.0000165798,0.0001465258],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8985711,0.0001814662,0.09893513,0.000028666,0.0002544443,0.0001209552,0.000001861696,0.0002081765,0.001698148],"genre_scores_gemma":[0.9366083,0.000003247362,0.06315983,0.00008518739,0.0000753969,0.000002222333,0.000001962034,0.00003804383,0.00002579539],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.046988,"threshold_uncertainty_score":0.4767031,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006959935862260248,"score_gpt":0.1936683593903205,"score_spread":0.1867084235280603,"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."}}