AT LONG LAST : ALTHOUGH THE RECENTLY ENACTED SURFACE TRANSPORTATION BILL PROVIDES RECORD LEVELS OF FEDERAL FUNDING...
Bibliographic record
Abstract
This article discusses the distribution of funds from the 2005 the Safe, Accountable, Flexible, Efficient Transportation Equity Act: A Legacy for Users (SAFETEA-LU). The discussion looks at SAFETEA-LU from the dynamics of donor and donee states to the earmarks given to important highway and public transportation projects. The bill, which allocated nearly 79.4 percent of its $286.5 budget to highway projects and about 18.5 percent for public transportation, marked about 2.2 percent ($6.3 billion) of its funding for highway safety programs. Under the category of “Projects of National and Regional Significance”, the article lists 24 projects, indicating the state(s) involved, project description, and amount of money allocated. A second chart titled “National Corridor Infrastructure Improvement Program” lists 33 projects of significance. The article names a few of the specific highway projects earmarked for special funding, such as $200 million for the Washington State DOT’s Alaskan Way Viaduct and Seawall Replacement; $125 million for the Alameda Corridor East project in Alameda County, California; finally, the additional capacity lanes that have been planned for Virginia’s I-81 have been designated to get about $100 million. The bill has also set aside quite a bit of funding for border region infrastructure to help facilitate cross-border cargo movement between the United States, Canada, and Mexico. Environmental organizations such as the Sierra Club have expressed some discomfort with certain regulations accompanying SAFETEA-LU, however, as it attempts to expedite the role of NEPA in the approval process for transportation projects by limiting the amount of time allowed for legal cases to be brought against the DOT for possible violations. The article also describes the stimulation this bill introduces for the public-private partnership market.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.086 | 0.048 |
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".