Performance evaluation of fog seals on chip seals and verification of fog seal field tests
Bibliographic record
Abstract
With an increasing emphasis on pavement preservation treatments due to economic concerns over the high costs of paving materials, one of the most cost-effective pavement preservation treatments, chip seals, now constitutes a significant proportion of the pavement preservation treatments used in the North Carolina highway network. To mitigate a major problem with chip seals, i.e., the loose aggregate particles, fog seals, which are composed of an emulsified product placed on top of the chip seal, can be used to help control the loose aggregate. For this study, fog seals were applied on top of newly fabricated chip. The surface texture of the fog-sealed chip seals was analyzed using the British pendulum test and a three-dimensional laser. Also, fog seal field test methods that were developed to suggest appropriate traffic opening times after fog seal construction were verified. The main findings presented in this paper are that: (i) the use of polymer-modified emulsions improves fog seal performance in terms of better aggregate retention and bleeding resistance; (ii) the skid resistance problems are not evident once the fog seal is applied on the recommended chip seal type; (iii) the relationship between skid number and mean profile depth can be determined based on three trends that are dependent on traffic loadings, and (iv) although the fog seal field tests were unable to be completed due to safety concerns, it can nonetheless be recommended that approximately 60 min after fog seal construction is an appropriate traffic opening time.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".