Innovations and development of sustainable personal protective equipment: a path to a greener future
Bibliographic record
Abstract
The unprecedented surge in the demand for personal protective equipment (PPE) worldwide during the covid pandemic resulted in a significant increase in PPE consumption and subsequent waste generation. Despite the importance of PPE, its widespread usage and disposal have sparked worries about the environmental impact and its long-term sustainability. The increasing awareness of environmental challenges, resource scarcity, and the urgent need to mitigate climate change necessitates a paradigm shift in the product design, manufacturing process, and waste management of PPE. To address these challenges and have a sustainable PPE future, the development of degradable polymers and natural fibers offers a promising alternative to traditional plastics. Additionally, recycling and upcycling methods can convert the waste into valuable alternate products or energy sources, thereby reducing their environmental impact. Better waste management systems, comprehensive policy frameworks, and international collaborations are essential for the effective PPE waste management and the promotion of sustainable practices. Despite the challenges, collaborative efforts across governments, manufacturers, research institutions, and waste management authorities are crucial for transitioning to a more sustainable PPE industry and a circular economy, ultimately benefiting both the environment and society.
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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".