{"id":"W4407409658","doi":"10.1177/09544097251318765","title":"Train-velocity-filtering of wayside noise to measure grind effectiveness when targeting variable wavelength rail corrugation","year":2025,"lang":"en","type":"article","venue":"Proceedings of the Institution of Mechanical Engineers Part F Journal of Rail and Rapid Transit","topic":"Railway Engineering and Dynamics","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada; University of Manitoba","funders":"","keywords":"Grind; Measure (data warehouse); Noise (video); Variable (mathematics); Wavelength; Acoustics; Computer science; Engineering; Mechanical engineering; Mathematics; Physics; Optics; Artificial intelligence; Data mining; Mathematical analysis; Image (mathematics); Grinding","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001001199,0.0006105533,0.0003890512,0.00232755,0.0003083269,0.0007673673,0.0005246758,0.0004661066,0.0009028784],"category_scores_gemma":[0.004204116,0.0001854017,0.0002809881,0.001598429,0.0002995469,0.0008957846,0.0004807403,0.0003641965,0.0003352415],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005623375,"about_ca_system_score_gemma":0.0006290337,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009868168,"about_ca_topic_score_gemma":0.03124793,"domain_scores_codex":[0.9990281,0.0001412425,0.00007346921,0.0001984187,0.0004862894,0.00007252469],"domain_scores_gemma":[0.9971634,0.0007222058,0.00075969,0.0003198207,0.0009307963,0.0001040914],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003090942,0.0005524828,0.5893005,0.0002812725,0.0002681104,0.0001520548,0.001086151,0.08803818,0.05754918,0.001628103,0.0012907,0.2595441],"study_design_scores_gemma":[0.00001622843,0.0008086905,0.6854984,0.00005121732,0.0001246016,0.0002698756,0.0008034735,0.2781808,0.02951038,0.001016347,0.003596318,0.0001237731],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7975264,0.0001231932,0.1964402,0.0000534702,0.00003743502,0.0001484489,0.0007985727,0.0006754515,0.004196775],"genre_scores_gemma":[0.9413388,0.00006058155,0.05679779,0.00001854935,0.00001118438,0.00008049316,0.0006931517,0.00005218812,0.0009471964],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009868168,"threshold_uncertainty_score":0.01962149,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006726263311549413,"score_gpt":0.1855703525958547,"score_spread":0.1788440892843053,"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."}}