Preventive-gradual on-cycle grinding: a first for MRS in Brazil
Bibliographic record
Abstract
MRS Logistica S.A. operates a 1674 km railroad in southeastem Brazil. Loads are primarily unit iron ore trains running from mines in the Brazilian mountains of Minas Gerais to tidewater harbors at Rio de Janeiro. Located in a region that concentrates 65% of the gross national product, MRS Logistica S.A. has experienced tremendous growth since privatization in the later part of 1996. Since that time the annual revenue tonnage more than doubled from 71 to 146 mmgt - metric millions gross tonnes (from 78 to160 mgt - millions gross tons) in 2003. Future plans call for another doubling of capacity to 304 mmgt (335mgt) by 2009. Prior to 2002 there were no rail grinding services at MRS. Under the strains of rapid growth, rail breaks and replacement due to RCF related surface defects were a daily occurrence, resulting in train traffic interruptions and significant increases in maintenance costs. Attempts were made to address the RCF conditions with a rail planer but in the end, rail replacement was the only alternative. This paper describes the strategies and actions taken to implement a cost effective, state-of-the-art grinding program at MRS in only 2 years. A partnership between MRS, Loram Maintenance of Way, Inc. and the National Research Council Canada was established to face the challenge. Specialized rail profiles for curve and tangent track were developed and implemented under an accelerated Preventive Gradual grinding program using a Loram RGI-48 stones rail grinder machine. The benefits of the rail grinding program will be detailed in terms of significant reductions in rail purchases, extended rail life, and reduced rail failures, fewer train delays and a 3% increase in fuel economy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".