Bibliographic record
Abstract
ABSTRACT: Grain transportation rationalization, economic diversification and value added initiatives within the Saskatchewan economy has, and will continue to, increase commercial truck traffic on many Saskatchewan roads. As a result, Saskatchewan Department of Highways and Transportation are investigating cold in-place recycling as a rehabilitation alternative for strengthening thin paved roads. However, different construction practices and years of maintenance and rehabilitation have led to many of these thin paved roads having variable structural composition. The effect of in situ variability on cold in-place recycle designs is further exacerbated by the inherent sensitivity of stabilizers such as asphalt emulsion, foamed asphalt, cementitious blends, and/or concentrated chemicals when integrated into different road materials. As a result, materials and structural design of cold in-place recycled thin paved road systems can be highly uncertain. Ground penetrating radar has been identified as an engineering diagnostic tool that can accurately quantify in situ structural composition and help reduce the uncertainty associated with material and structural design of cold in-place recycled road systems. This paper summarizes the principles of ground penetrating radar, discusses the use of ground penetrating radar as an engineering diagnostic tool for cold in-place recycling of thin paved roads, and presents two pilot case studies undertaken by Saskatchewan Department of Highways and Transportation that demonstrates the
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.664 | 0.606 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".