Environmental Reviews & Case Studies: Integrating High-Speed Rail into North America's Next Mobility Transition
Bibliographic record
Abstract
Although the American Recovery and Reinvestment Act of 2009 provided $8 billion in federal grants for passenger rail development, no national policy framework yet exists to guide this investment. Integrating new high-speed rail lines with existing railroad infrastructure and connecting them with air, road, and transit systems will be of utmost importance. As most of the world's cheap and accessible oil has already been consumed, transportation modes that depend exclusively on oil can be expected to decline, while energy-efficient, low-impact modes such as passenger trains will advance. A new model railroad that shifts more passenger travel to rail will develop through an incremental adaptation of current passenger services, a comprehensive transformation using high-speed train technology, or a combination of these two trajectories. North American designs for high-speed rail will need to incorporate the European innovation of providing multiple air, auto, and rail connections in the form of four station types: the city-center and airport stations seen in Europe, as well as suburban stations at business parks and at suburban commercial centers. To create an effective synergy between transportation and local land use, three broad categories of policy tools will need to be deployed.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".