INDUSTRY OUTLOOK : THIS YEAR'S ECONOMY DIDN'T CATCH RAIL EXECS FLAT- FOOTED
Bibliographic record
Abstract
The rail industry has been pushing growth where it can find it to offset losses. For example, U.S. freight railroads raised their intermodal and automotive shipments by roughly 4%, but grain dropped by the same amount. In Canada, intermodal and carloads were up about 10%, but grain fell 18.5%. Passenger lines have expanded in some places and increased ridership on some lines, but they had to raise fares and cut staff to meet the budget cuts from states. The next year, 2003, doesn't seem to be much better. For Class 1 railroads, increasing revenue is a function of wooing customers to their lines with better service, rather than competing on rates. One line is replacing older locomotives to increase its reliability. As productivity is added, staff can be cut, reducing costs. Another hauler has created corridor products to attract regional shippers. Another is offering a guaranteed service and wireless tracking of individual shipments. To boost productivity they are using more remote-control locomotive units and creating bridge routes between different lines. With the economy still in decline, passenger lines are pinning new growth on bringing new riders on board, not just for commute trips but for other journeys.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.107 | 0.067 |
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".