Plastics from the end-of-life perspective
Bibliographic record
Abstract
Plastic environmental pollution, as an emerging menace for ecosystem functioning, is targeted by a broad audience. Recently, research studies have been focused on plastic waste accumulation, pollution, and innovative solutions for plastic waste management due to their vital role in protecting the environment. This study focuses on the global plastic pollution problem, especially single-use plastic, which is named as a lifesaver during the coronavirus disease 2019 (Covid-19) pandemic. It also addresses the latest scientific insights and reliable solutions such as moving from a linear economy to a circular economy as a significant cost-saving achievement. In this perspective, plastic pollution surrounds the earth’s ecosystem, ocean, soil, and air, which is a severe threat to the environment and human health. In this regard, less-explained scenarios, as well as simple, practical methods for overcoming the problem or reducing its consequences, are described. The results of this study show the following to solve the plastic pollution crisis: significant changes in human habits and training towards “4Rs” (reducing, reusing, recycling, and refusing plastics), comprehensive government management, on the one hand, by developing the necessary standards to limit the production of single-use plastic, and on the other hand, by creating more incentives for the development of more cost-effective prevention and collection technologies, as well as the production of reusable packaging with Use of 100% renewable energy and wider efforts to produce biodegradable plastics, and finally, implementing circular economy or zero-waste approaches.
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 | 0.002 |
| 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 | 0.003 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 0.006 |
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".