Full-Depth In-Place Recycling and Road Strengthening Systems for Low-Volume Roads: Highway No. 19 Case Study
Bibliographic record
Abstract
The province of Saskatchewan has the highest number of public roads per capita in Canada, totaling approximately 198,700 km. The Saskatchewan Department of Highways and Transportation manages approximately 26,100 km of these public roads. As with most public road agencies, the department has limited resources for managing this relatively large network of low-volume roads and therefore continually strives to research and implement more cost-effective and technically feasible solutions. Of particular concern are approximately 7,500 km of thin membrane surface (TMS) roads that are undergoing accelerated damage as truck traffic and loading increase. Although conventional TMS upgrading strategies are normally effective, there are potentially significant benefits to be gained with implementation of more cost-effective methods of road strengthening. Systems include granular soil strengthening and applications of different cement products, lime, various grades of fly ash, geotextiles, geogrids, natural and manufactured fibers, emulsified bitumen, tall oil, lignin, foamed bitumen, and synthetic ionic and cationic chemicals. Since 1999, in conjunction with Pavement Scientific International and in cooperation with the University of Saskatchewan and product suppliers, the department has constructed pilot projects on Highway No. 19 to identify, develop, and implement more cost-effective strengthening systems. The primary research objective is to investigate alternative road construction systems that will improve the load-carrying capacity and environmental durability of Saskatchewan TMS roads. Improved mechanistic engineering methods played a part in this initiative, and pilot project performance was monitored.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".