Circular economy-based closed-loop supply chain for sustainable winter road maintenance: A case study in Trois Rivières
Bibliographic record
Abstract
This research addresses the environmental challenges posed by the disposal of winter road abrasives in regions with severe winters, such as Quebec. Road authorities apply abrasives like sand and crushed stones for safety during winter, leading to the collection of residual materials known as street sweeps in spring. Currently, a significant portion of these sweeps is disposed of through burial, prompting the need for a sustainable solution. The study focuses on a closed-loop supply chain (CLSC) model for abrasive management, introducing the concept of the circular economy to evolve residual materials. Two main actors, the Trois Rivières municipality and the Québec Ministry of Transportation, play the roles of both suppliers and customers. They contribute to a recycling center where the sweeps are processed, reusable abrasives are separated, and applied to producing recycled abrasive. A Mixed-Integer Linear Programming (MILP) model is developed for CLSC optimization. The model considers key components including quarries supplying sand and crushed stones, a winter maintenance center for mixing and storage, a customer zone offering a free market, road abrasive spreading, and road infrastructure construction/repair. The recycling center employs sorting and mixing processes, directing the recycled abrasives to either the winter maintenance center or landfill. This research emphasizes the potential of the CLSC model to not only reduce operational costs but also provide environmental advantages by diverting materials away from landfills. The MILP model serves as a strategic tool for optimizing the entire process, contributing to a more sustainable approach in winter road maintenance practices.
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 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".