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
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.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it