Preventive grinding on Estrada de Ferro Carajás, Brazil
Bibliographic record
Abstract
Vale S.A.’s Estrada de Ferro Carajás (EFC) and Transportation Technology Center, Inc. (TTCI) began a program in 1996 to increase tonnage on EFC’s 890 kilometres of track. Significant rail and wheel fatigue problems were causing excessive maintenance and removal of fatigued components. By 2016 EFC will increase line capacity by installing double track, increasing axle loads to 37.5 tonne (t) and increasing annual tonnage to 460 million gross tonnes (mgt). The National Research Council Canada, Surface Transportation (NRC-ST) was commissioned in 2008 by TTCI and EFC to assist with the management of the rail to increase rail life. NRC-ST designed six new rail profile templates. Multiple tangent profiles were introduced to spread the distribution of rail contacts across the wheel tread to obtain more uniform tread wear which reduces the rate of tread hollowing and the development of false flanges. To better manage the rail and increase grinding productivity, EFC purchased a high production 96-stone rail grinder in 2008 to better implement the NRC-ST rail profile templates using a preventive gradual grinding strategy. A detailed grinding program was developed using a single pass at 15 km/h with specific patterns for each tangent and curve on EFC. Grinding intervals were established based on the rail position and the state of rolling contact fatigue (RCF). The EFC rail is now in excellent condition after 450 mgt of traffic. Newer rail on the line is now predicted to last significantly longer as a result of the preventive grinding program. Starting in 2012 curve rail life will further increase with the introduction of an effective lubrication and top-of-rail friction management strategy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 source (direct Gemma or distilled Codex), 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".