Cleaning Up without Messing Up: Maximizing the Benefits of Plastic Clean-Up Technologies through New Regulatory Approaches
Bibliographic record
Abstract
As the global plastics crisis grows, numerous technologies have been invented and implemented to recover plastic pollution from the environment. Although laudable, unregulated clean-up technologies may be inefficient and have unintended negative consequences on ecosystems, for example, through bycatch or removal of organic matter important for ecosystem functions. Despite these concerns, plastic clean-up technologies can play an important role in reducing litter in the environment. As the United Nations Environment Assembly is moving toward an international, legally binding treaty to address plastic pollution by 2024, the implementation of plastic clean-up technologies should be regulated to secure their net benefits and avoid unintended damages. Regulation can require environmental impact assessments and life cycle analysis to be conducted predeployment on a case-by-case basis to determine their effectiveness and impact and secure environmentally sound management. During operations catch-efficiency and bycatch of nonlitter items, as well as waste management of recovered litter, should be documented. Data collection for monitoring, research, and outreach to mitigate plastic pollution is recommended as added value of implementation of clean-up technologies.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".