Circular Coffee Shops' Smart Approach in Montreal
Bibliographic record
Abstract
As coffee consumption continues to rise, it is becoming increasingly essential to integrate coffee waste into the circular economy framework and promote sustainability. However, uncertainties can complicate efforts to manage circularity in the coffee value chain. To address these issues, we need an innovative and sustainable system that streamlines the process while minimizing adverse effects on the environment, society, and product quality. One potential solution is to establish circular coffee shops (CC) as local depots in the coffee waste collection network that are equipped with pre-drying technologies. Considering their social and environmental impacts, this article examines the optimal location, allocation and routing decisions for CC. Our proposed business model aims to design a coffee waste collection network that minimizes costs, and we also explore different scenarios for the robustness of the results and the project's financial outlook. Based on our model's results in Montreal City, we conclude that designing circular coffee shops reduces economic and environmental impacts. Incorporating Industry 4.0 technologies into management practices holds tremendous potential for driving sustainability and circularity in the coffee value chain.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.015 | 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".