Analyzing Longitudinal Data to Demonstrate the Costs and Benefits of Pavement Management
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Roads and highways generally represent the single largest asset value of public infrastructure. Preservation of this asset value through timely and cost-effective maintenance and rehabilitation presents an enormous financial, management, and technical challenge to public agencies. Until recently, agencies have relied on designated or “silo” systems for pavement, bridge, and other management systems; which shared common elements of data collection, analysis, and reporting. Successful implementation of asset management requires a methodology for trade-off analysis between competing silos at the strategic level. Ultimately, many agencies may need to significantly change their business decision-making process, potentially resulting in the costs of implementation outweighing the benefits. This article describes frameworks for using longitudinal data to conduct a cost-benefit analysis of management system implementation. It also demonstrates how the same data can be used to improve technical models, thereby producing immediate benefits to the agency through enhanced decision making and, ultimately, reduced costs.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it