{"id":"W7131912363","doi":"","title":"Implementation of preventive rail grinding on Fortescue Metals Group 40 tonne axle load railway","year":2015,"lang":"en","type":"article","venue":"NPARC","topic":"Railway Engineering and Dynamics","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Grinding; Tonne; Train; Track (disk drive); Axle; Revenue; Railway system; Rail transportation","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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":[0.0003239403,0.0001621788,0.0002192729,0.0001024732,0.00002265078,0.00001766197,0.0001208172,0.00006133313,0.00008778036],"category_scores_gemma":[0.00003236103,0.0001639891,0.00007985429,0.0001711277,0.00001514665,0.0001467272,0.00002467006,0.0001148785,0.00002741256],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001779009,"about_ca_system_score_gemma":0.00002532265,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002344541,"about_ca_topic_score_gemma":0.00005133227,"domain_scores_codex":[0.9990296,0.00001820361,0.0002711004,0.0001509246,0.0002767529,0.0002533928],"domain_scores_gemma":[0.9995444,0.00004553133,0.00004565395,0.0002046209,0.00005683872,0.0001029172],"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":[0.0001615274,0.0002571982,0.005252587,0.0006574473,0.0009910988,0.00003019852,0.008503369,0.5780108,0.2562877,0.03235483,0.01223889,0.1052543],"study_design_scores_gemma":[0.0131453,0.002226487,0.05839495,0.0006219603,0.000516369,0.00005175301,0.00832933,0.4579649,0.4110423,0.02076175,0.0238455,0.003099452],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9395216,0.00009332828,0.0368311,0.00002415727,0.000597318,0.0002715156,0.00004927441,0.0002707491,0.02234095],"genre_scores_gemma":[0.9955226,0.00001762923,0.004025982,0.000008720634,0.00008010092,0.00003236344,0.00003481064,0.00004039999,0.0002374467],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1547546,"threshold_uncertainty_score":0.668728,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0144413605500886,"score_gpt":0.2496550071285565,"score_spread":0.2352136465784679,"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."}}