Estimation of Investment in Track and Structures Needed to Handle 129 844-kg (286,000-lb) Railcars on Short-Line Railroads
Bibliographic record
Abstract
Ownership of the U.S. rail industry is divided between eight Class I railroads (those with more than $258.5 million in annual revenue) and about 550 regional and short-line railroads. The eight large railroads own about 70 percent of the 273 700 track-km (170,000 track-mi) and account for about 90 percent of industry revenues. The remaining 30 percent of track kilometers belongs to the regional and short-line railroads, which must operate and maintain them with 10 percent of industry revenues. U.S. railroads function as an integrated network; freight originating on a short-line railroad can be delivered anywhere in the United States, Canada, or Mexico. Equipment is freely interchanged, so the small railroads must handle the same heavy cars as the Class I railroads even though maximum freight car weights have increased in recent years, with cars of 129 844 kg (286,000 lb) becoming common. Many of the smaller railroads own trackage that had been branchlines belonging to the larger companies, and track components and condition are often marginal or inadequate to handle the heavier loads. Yet, if short lines cannot handle heavier cars, they face a loss of revenue and ultimately business failure. ZETA-TECH conducted a survey of short-line and regional railroads to determine the quantities of track materials, bridge repairs, and replacements needed to handle heavier cars. Using standard railroad industry unit costs, ZETA-TECH estimated the cost of this work at $6.86 billion in 1999 dollars.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".