Bibliographic record
Abstract
Typically, the costs for traction energy add up to 20% or more of the total train operating costs with electric locomotives in Europe. Therefore, there is a high incentive for the railroads to reduce energy consumption and thus to improve operating margins. Additionally, rising costs for energy as well as environmental aspects will increase the need to reduce energy consumption in the future. Firstly, on electric locomotives the largest energy savings are obtained from power regeneration at braking. In this mode the locomotive acts as a moving power generator feeding energy back into the catenary network. Savings are typically in the range of 10 to 30%. Secondly, the driving style has a high impact on energy costs. Energy consumption can be lowered by more than 20% through an energy conscious driving style compared to aggressive driving. Thirdly, the energy efficiency of the whole traction chain is important. Electric locomotives designed for AC catenaries have an overall energy efficiency of up to 86%. Locomotives designed for 1.5 or 3 kV DC catenaries can have an overall energy efficiency of up to 90%. New technologies can potentially help to increase the power efficiencies even further. Apart from using efficient diesel engines, the fuel costs of diesel-electric locomotives can be reduced much in the same way as with electric locomotives. Regeneration of braking power on diesel-electric locomotives is, however, limited to feeding the auxiliaries and head end power (HEP) to passenger coaches. In Europe the energy costs per hauled ton-km are typically much lower with electric than with diesel traction. This gives dual-powered locomotives the advantage of overall lower energy costs for operation on both electrified and non-electrified networks. First estimates show that the total energy costs (diesel and electric operation) can be reduced by more than 35% in a mixed network with 80% electrification with a dual-powered locomotive compared to a diesel locomotive running the same train on the same route. In addition, the dual-powered locomotive provides major cost savings and increased quality of service with a one seat ride.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.016 | 0.005 |
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".