Incentivized decarbonization through safer and more efficient heavy haul operations
Bibliographic record
Abstract
The improvement in component service life and railway service reliability has been viewed from an environmental perspective. Delays and train accidents carry enormous costs and greenhouse gas (GHG) emissions potential due to the increased fuel burn and the need to replace heavy equipment prematurely. The use of higher-performance materials, more efficient vehicle design, and modern technology by Canadian railways to reduce service interruptions are presented. Their resulting cost savings not only can provide financial incentives for the continuous optimization of asset utilization but can also lead to significant contributions to the decarbonization of the railway industry. The authors have estimated a total reduction of 2.4 kt-CO 2 e per year in embodies carbon emissions due to life extensions of wheelsets compared to 2017 levels on a Class 1 railway. System-wide, the rail life extension has resulted in a saving of 8.1 kt-CO 2 e per year compared to 2017 for the same railway. Compared to 2004, the Canadian railway industry has achieved an annual reduction in embodied carbon emission of 6.7 Mt-CO 2 e from the reduction in mainline derailments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".