Adoption and diffusion of marine litter clean-up technologies across European seas: Legal, institutional and financial drivers and barriers
Bibliographic record
Abstract
This study reviews existing legal, institutional and policy tools and frameworks, relevant to the introduction and adoption of new marine litter clean-up technologies in two regional European seas, the Mediterranean and the Baltic. A combination of desk studies in six countries bordering the Baltic (Estonia, Germany, Sweden) and the Mediterranean (Greece, Italy, Tunisia), and interviews with experts and stakeholders, is used to identify key drivers and barriers to the adoption and diffusion of marine litter technologies. The main conclusion of the study is that the most influential pieces of legislation relevant to marine litter management are top-down EU policies, often forming the basis of regional and national plans. Moreover, the study finds that several drivers of marine litter technologies may at the same time be critical barriers. These factors include public awareness, consumer behaviour, enforcement of legislation, and the rise of SMEs engaged in recycling and eco-labelling of marine litter.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".