Bibliographic record
Abstract
This article describes how the railroad industry has emerged as one of America’s great growth industries this year, which is not bad for an old “brick and mortar” industry that’s nearly 200 years old and was pronounced ready for the scrap yard as recently as 1980. Even some of Wall Street’s wisest executives are wondering how they did it. The difference between now and then is that the railroads have been able to run their businesses with the same degree of market freedom as most other industries since deregulation in 1980. Railroads have shown remarkable resilience in the face of adversity this year. The railroads’ ability to quickly match the size of their operations to the size of their business and the freedom to price their services according to what the market will bear, rather than by a complex set of government imposed regulations have contributed to this growth. This growth may very well end up as another record year in terms of revenues and profits. Coal may have dropped precipitously in the first half of this year, but the resilient railroads have gained footholds in other markets to more than make up the difference. The intermodal and automotive markets are two good examples. Another is petroleum products. The article presents the growth results from CSX, Norfolk Southern, Kansas City Southern, Canadian Pacific and CN railroads.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.216 | 0.061 |
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".