Results of Long-Term Pavement Performance SPS-3 Analysis: Preventive Maintenance of Flexible Pavements : [techbrief]
Bibliographic record
Abstract
This document is a technical summary of the Federal Highway Administration report, Impact of Design Features on Pavement Response and Performance in Rehabilitated Flexible and Rigid Pavements (FHWA-HRT-10-066). Rehabilitation and pavement preservation represent the majority of pavement construction activity in the United States. Preventive maintenance includes treatments that are applied to pavements primarily to delay development of and mitigate existing distresses. These treatments focus on improving pavement functional performance and prolonging pavement life, not on improving the structural capacity. Selecting the appropriate maintenance technique and treatment application timing form the basis of a preventive maintenance practice. In addition to a nontreated control section, the Specific Pavement Study (SPS)-3 experiment included the following four maintenance treatment alternatives: Thin hot mix asphalt overlay (typically 1 inch (25.4 mm) or less). Slurry seal. Crack seal. Chip seal. Additionally, each site was categorized according to the following five design factors: Moisture (wet or dry climate). Temperature (freeze or no-freeze zone). Subgrade type (fine grained or coarse grained). Traffic loading (low or high). Existing pavement condition (good, fair, or poor). This experimental design resulted in 48 different experimental combinations of factors. In total, 33 States and Canadian Provinces participated in the experiment, and 81 sites were constructed and monitored for the assessment.\n
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".