A2M05: Committee on Guided Intercity Passenger Transportation, Intercity Passenger Rail. Available online at: http://onlinepubs.trb.org/onlinepubs/millennium/00059.pdf IMPROVERAIL
Bibliographic record
Abstract
While the state of the art of intercity passenger rail (IPR) has advanced steadily worldwide during the past quarter-century, there is immense potential for further improvements in North America given the underutilization of and limited investment in this mode to date. Europe and Japan have advanced their IPR systems well beyond those in the United States, Canada, and Mexico. If North America is to remain truly competitive in the global marketplace, it must invest in a world-class transportation system that includes IPR, an important element of which is high-speed ground transportation (HSGT). HSGT—a family of technologies ranging from upgraded existing railroads to magnetically levitated vehicles—is the most efficient mode for moving large volumes of people between metropolitan areas lying about 100–500 miles apart. In the United States, HSGT already exists in the Northeast Corridor. There are from 6 to 12 potential HSGT corridors in North America (depending on how they are defined), where investment in HSGT is commercially feasible (1). CHALLENGES AND ISSUES At the dawn of the new millennium, IPR is reemerging in North America as not just viable, but essential to the improved mobility of the densely populated Northeast, Southeast, West Coast, and Midwest (Chicago Hub) corridors, as well as of other potential emerging corridors in the Gulf Coast states, Texas, and Eastern Canada. There are nevertheless many challenges to be overcome before the development of IPR in these corridors can be realized. Most of these challenges are not technological, but political, institutional, and financial. Yet resolution of the technological issues can aid in addressing the other issues as well. Technology Transfer
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.163 | 0.161 |
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".