The I-95 Corridor in the United States: Drawing Benefits from Intermodality
Bibliographic record
Abstract
The Interstate 95 Corridor in the United States is 1,919 miles long and traverses 15 states, from the Canadian Border in Maine to the Southeast corner of the United States in Florida. The 15 states on the I-5 corridor also contain 31,000 miles of rail lines, both freight and passenger, 46 major seaports, and 103 commercial airports. If the states on the Corridor were a separate country it would constitute the second largest economy in the world. Population growth and economic growth have put an increasingly heavy burden on all modes of transportation. In response the I-95 Corridor was formed, initially as a means of coordinating on intelligent transportation systems initiatives across states lines. It has evolved into an institution that provides a forum for key decision and policy makers to address transportation management and operations issues of common interest, with a high priority for relieving congestion on the I-95 Interstate Highway by diverting freight to other modes. The I-95 Coalition has undertaken a number of studies to assess capacity and performance of its highway, rail, and maritime modes. The I-95 case is the leading example in the United States of a coordinated effort to address the transportation challenges arising from increasing congestion and constrained capacity in a large region. For the covering abstract see ITRD E139491.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".