Guidance on the monitoring of marine litter in European seas – An update to improve the harmonised monitoring of marine litter under the Marine Strategy Framework Directive.
Bibliographic record
Abstract
The Marine Strategy Framework Directive (MSFD) Technical Group on Marine Litter developed the ‘Guidance on monitoring of marine litter in European seas’ in 2013 to enable EU Member States to launch monitoring programmes for MSFD Descriptor 10: ‘no harm caused by marine litter’. The maturity of methodological protocols for marine litter monitoring has increased over the last 10 years, based on research advances and Member States’ efforts. This document updates the previous guidance to facilitate the harmonisation of the monitoring framework for the MSFD, including protocols, recommendations, and information required to increase the comparability of data and assessments among Member States. The document comprises chapters dedicated to the protocols for monitoring marine litter across different marine environmental compartments (i.e. the coastline/beach, the surface layer of the water column, the seafloor/seabed) and types of litter (i.e. macro litter, mesolitter, microlitter, ingested litter and microlitter by biota, and entanglement with litter).
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 | 0.027 | 0.019 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.011 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.007 |
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".