Bibliographic record
Abstract
The annul survey of rail projects by METRO magazine puts New Jersey Transit at the top of the funding list for U.S. and Canadian rail projects for 2009. It has $14.6 billion worth of projects. The total funding for the top 10 is $70 billion, with American Recovery and Reinvestment Act (ARRA) funds providing a large increase. New Jersey Transit’s showing displaces the perennial number one occupant, New York City Transit. This year it occupies the number two spot, with a little over $8.1 billion. Other notable changes in the rankings include Seattle’s Sound Transit, which is now fourth in spending, up from 8th. Denver’s Regional Transportation District (RTD) and the San Francisco Municipal Railway are three and five, respectively. New Jersey Transit is receiving $424 million under the ARRA. One of its largest undertakings is the Mass Transit Tunnel (MTT) project, which will produce two new trans-Hudson commuter rail tunnels and an expanded Penn Station. Across the survey, funding is up nearly $20 billion from 2008 and is the largest since 2004. The railcar fleet mix continues to be dominated by heavy rail, at 59 percent, and it appears that they will remain the vehicle of choice in the future. The MTA’s New York City Transit has the largest rail fleet, with 6,774 railcars, followed by Amtrak, with 1,545.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.005 | 0.000 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.096 | 0.032 |
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".