Bibliographic record
Abstract
In the business environment of today where railroads are competing head to head with trucks, and often in a tightly structured logistics chain, providing transportation in tandem with those competitors, improved equipment utilization is a large part of the drive to get more capacity out of existing infrastructure. Equipment utilization and asset management mean virtually the same thing: positioning cars, locomotives and crews to support an operating plan. Equally important is: how cars and locomotives are maintained, inspected, and repaired, and to have the tools the mechanical forces employ to do the most efficient job possible to keep the assets moving and perhaps create additional capacity. One of the important aspects to equipment utilization/asset management is scheduling. The focus of this article is on examination of how scheduled operations wrest more work from existing fleets. Highly sophisticated computer modeling tools and computer based operating systems, combined with common sense approaches are helping railroads improve their equipment utilization, whether it is for boxcars or boxes. Systems like Norfolk Southern's (NS) Thoroughbred Operating Plan (TOP) and service products like Canadian National's (CN) Intermodal Excellence (IMX) have become industry standard. The article discusses NS and CN asset management systems as the best examples for building capacity through well planned equipment utilization. For NS, Top has provided enormous benefits, not the least of which is this railroad's ability to handle unprecedented traffic growth. For CN, a new precision intermodal product and service plan called IMX has boosted train capacity by more than 20%.
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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.006 |
| Scholarly communication | 0.016 | 0.018 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.122 | 0.087 |
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".