Economic feasibility analysis of a medical mask closed-loop supply chain: A Canadian case study
Bibliographic record
Abstract
The world is being exposed to a global health crisis due to covid-19. This situation is generating an unprecedented \nincrease in the use of single-use medical materials, notably procedural facemasks. This study focuses on the design and \nplanning of a closed-loop supply chain (SC) for dealing with end-of-life procedural facemasks. An optimization model to \nefficiently collect and recycle used procedural facemasks is proposed. The main benefits are the correct disposal of \ncontaminated products and component recycling. The considered SC network includes virgin raw material, suppliers, \nfacemask manufacturing centers, warehouses, distribution centers, business clients, collection centers, dismantling and \nrecycling centers, and finally, clients for the recycled components. Decisions to be made include material flows in the \nnetwork, supplier and facility (collection and recycling centers) selection in order to maximize the profit of the SC. A realistic \ncase study is created based on real data gathered from different industrial partners in the Montreal region. Various \nscenarios are analyzed to identify the conditions under which the SC is profitable.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".