Bibliographic record
Abstract
As railroad system capacity, fluidity, and velocity are becoming better understood, class yards are becoming to be seen as even more vital components of the whole process of precision railroading than many professionals have believed. Achieving maximum yard throughput that can also increase capacity is a delicate balancing act, a blend of operations strategy and technology. This article looks at questions and answers that are evolving about what hump and flat class yards do, and what processes, information technologies, and hardware are necessary to make loose car railroading as efficient and reliable as intermodal. Some of the approaches crafted to the companies' unique needs include: Burlington Northern and Santa Fe's program Operation Pentagon that has brought about the greatest change in the nature of class yard operation; Canadian Pacific's Yard Operations performance team, with the key objective of bridge building between yard and road operations to reduce terminal dwell time; and Norfolk Southern's Buckeye Yard that had its 30 year old equipment replaced by Trainyard Tech's HC41 hump control system that is based on creating an open systems environment with off the shelf, Windows based architecture.
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.016 | 0.013 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.036 | 0.021 |
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".