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Record W569910325

SAFETEA-LU and Its Impact on the Bridge Market

2006· article· en· W569910325 on OpenAlexvenueno aff
Shanon Fauerbach

Bibliographic record

VenueBridges Conversations in Global Politics and Public Policy · 2006
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicPublic-Private Partnership Projects
Canadian institutionsnot available
Fundersnot available
KeywordsFinanceBridge (graph theory)RevenueEquity (law)BusinessTax revenueState highwayTransport engineeringEngineeringEconomicsPolitical science
DOInot available

Abstract

fetched live from OpenAlex

The Safe, Accountable, Flexible, Efficient Transportation Equity Act--A Legacy for Users (SAFETEA-LU) is the largest public works bill in U.S. history. This article provides a brief introduction to SAFETEA-LU, highlighting its highway bridge program. The highway bridge program provides funding to enable states to improve the condition of their highway bridges through replacement, rehabilitation and preventative maintenance. The eligible use of funds is expanded under SAFETEA-LU to include systematic preventative maintenance on federal-aid and non-federal-aid highway systems. States may carry out projects for the installation of scour countermeasures or systematic preventative maintenance without regard to whether the bridge is eligible for replacement or rehabilitation. SAFETEA-LU authorizes the highway bridge program at $25.6 billion over 6 years, a 25.5% increase over previous funding. SAFETEA-LU continues a set-aside of not less than 15% of the amount apportioned to each state in each fiscal year for use on bridge projects that are not on federal-aid highways. Funds are distributed according to the existing formula based on each state's relative share of the total cost to repair or replace deficient highway bridges. Much of the financing of surface transportation projects under SAFETEA-LU comes from federal gas tax revenue that is deposited in the Highway Trust Fund. This system leaves many needed projects unfunded, leading project owners to explore some innovative financing mechanisms outside of SAFETEA-LU to pay for needed transportation infrastructure projects.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.859
Threshold uncertainty score0.978

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.027
GPT teacher head0.288
Teacher spread0.260 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2006
Admission routes1
Has abstractyes

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