How Increasing Industry Competition Benefits the Pavement Market And How Agencies Can Use It to Lower Their Pavement Expenditures
Bibliographic record
Abstract
With the passing of the Bipartisan Infrastructure Law in 2021, the US is poised to spend billions of dollars on its highways and roadways. Despite this enormous outlay of funds, the US highway infrastructure needs are still at an all-time high. There is about a $435 billion backlog of highway road repair projects, and over 40% of US roadways are in a poor/deficient condition, which is costing the country an additional $130 billion in extra vehicle repairs and operating costs, or over $1,000 per motorist per year. Improvement of the system is needed. The primary approach to address this challenge has typically been to increase funding. While more funding helps, agencies also need to be more efficient within their constrained budgets to get more out of their roadway and pavement investments. This paper will show how competition across paving industries can be used to lower pavement unit costs.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 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".