Improving policy to support wider uptake of marine litter clean-up technologies across European seas: the CLAIM contribution
Bibliographic record
Abstract
Marine litter pollution, including plastic litter, is a major environmental challenge of our time with significant impacts on marine ecosystems and socio-economic costs on various sectors, including tourism and recreation, aquaculture, fisheries and commercial shipping. Numerous national laws, policies, initiatives and funds exist to address marine litter, but these are often part of broader regulatory frameworks and without a direct link to best available technologies to either prevent litter from entering the sea, or to clean it up once it has reached the sea. The distinction between macro and micro plastic litter is crucial in this context, since they require different technological and governance solutions, including for their clean-up. Whilst the best way to tackle plastic marine litter is to prevent it from reaching the sea at all, unless and until full prevention is achieved, appropriate technologies and policies are still needed to enable litter clean-up. Existing policies provide an important regulatory framework for assessing the extent of the marine litter problem in general. For example, marine litter is included as an indicator that must be monitored under the EU’s Marine Strategy Framework Directive (MSFD). However, the link to concrete actions addressing different types of litter from different sources is often lacking. Exceptions include the marine litter action plans developed under the Regional Sea Conventions, which have an important role in implementing the MSFD, or the 2019 Port Reception Facilities Directive (PRFD). Nevertheless, additional policies and incentives are still needed to tackle the plastic pollution problem. To encourage the wider use of best available marine litter clean-up technologies, we recommend that: (1) existing laws and policy approaches are adapted to provide coherent legal mandates for the actual clean-up of different types of marine litter, including an explicit reference to marine litter in relevant legislation; (2) new policy targets are set to limit macro and micro plastics in fresh and marine waters, and existing and new targets monitored to ensure they are met; (3) funding and financial incentives, and economic instruments, are mobilised to support the wider use of proven cost-effective clean-up technologies; (4) coordination and clear allocation of responsibilities is ensured between different levels of government and other responsible entities; and (5) platforms are created for more systematic, active engagement of key public and private sector stakeholders, to inform policy development, implementation and revision in support of the best use of advanced technical and technological knowledge.
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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 | 0.022 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.013 | 0.005 |
| Insufficient payload (model declined to judge) | 0.018 | 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".