Bibliographic record
Abstract
Water is one of the most detrimental elements to any pavement structure. Moisture related deterioration of Hot Mix Asphalt (HMA) is commonly referred to as stripping. Stripping occurs when the bond between the asphalt cement and the aggregate break down due to the presence of moisture. The only effective means of continuing to use moisture sensitive aggregates, therefore, is to strengthen this bond. Nova Scotia has typically utilized liquid anti-stripping additives to combat the problem of HMA stripping throughout the province. During the 2005 construction season, Dexter Construction Company Limited completed three Nova Scotia Transportation and Public Works (NSTPW) contracts from a known moisture susceptible aggregate source and incorporated hydrated lime as an anti-stripping additive instead of a commonly used liquid additive. This marked the first use of hydrated lime on NSTPW projects. This paper briefly describes the benefits of utilizing hydrated lime in HMA and the most common methods of introducing the lime to the mix. The main focus of this paper concentrates on why the contractor decided to utilize hydrated lime, the pros and cons associated with its use, and the effectiveness of hydrated lime as an anti-stripping additive based on the analysis of field sampled HMA.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".