Bibliographic record
Abstract
Accurately predicting performance and durability is critical to improving pavement design. Since 1987, the Federal Highway Administration's (FHWA) Long-Term Pavement Performance (LTPP) program, the most comprehensive pavement research program ever undertaken, has addressed the issues of improving pavement performance and optimizing the Nation's investment in the surface transportation system. This article describes the LTPP program, including its history, goals, successes and future plans. FHWA researchers work in partnership with state and provincial departments of transportation (DOTs) to gather and analyze data from 2,500-plus test sections across the United States and southern Canada. The LTPP program relies on pavement test sections constructed on public roads in all major climate zones and soil types. The main task of the LTPP program is to understand the effects of variations in loading, environment, material properties, construction variability, maintenance, and rehabilitation on pavement performance. A plan has been developed for data collection that links user needs to data requirements and provides guidelines to help transportation agencies and researchers measure data accurately and on a regular basis. The end goal is to develop a knowledge base to help advance management and engineering tools to extend pavement life on the interstates and other roadways. The LTPP program collates and releases an updated database annually and distributes analysis findings via publications and reports throughout the year to help manage existing pavements and inspire research into the pavements of tomorrow. FHWA management has announced publicly its commitment to continue monitoring existing test sections and to be custodian of all LTPP data and information until at least 2015.
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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.258 | 0.256 |
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".