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Record W4412227737 · doi:10.2760/59137

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.

2023· report· en· W4412227737 on OpenAlexaff
François Galgani, Luis F. Ruiz-Orejón, Francesca Ronchi, Kévin Tallec, Elke Kerstin Fischer, Marco Matiddi, Aikaterini Anastasopoulou, Eva Andresmaa, Michela Angiolillo, Martha Bakker, Andy M. Booth, Natalja Buhhalko, Bernard Cadiou, Françoise Claro, Pierpaolo Consoli, Gaëlle Darmon, Salud Deudero, David M. Fleet, Tomaso Fortibuoni, María Cristina Fossi, Jesús Gago, Olivia Gérigny, Alessandra Giorgetti, Daniel Pérez González, Nils Guse, Mirco Haseler, Christos Ioakeimidis, Ulrike Kammann, Susanne Kühn, Camille Lacroix, Inga Lips, Liria Ana Loza, Maria Eugenia Molina Jack, Katja Norén, Michail Papadoyannakis, Hannah Pragnell-Raasch, Anna Rindorf, Marta Ruiz, Outi Setälä, Marcus Schulz, Martin Schultze, Lone Soederberg, Elena Stoica, Marie Storr‐Paulsen, Jakob Strand

Bibliographic record

Venuenot available
Typereport
Languageen
FieldEnvironmental Science
TopicMicroplastics and Plastic Pollution
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsMarine Strategy Framework DirectiveMarine debrisLitterDirectiveEnvironmental scienceWater Framework DirectiveFisheryEnvironmental resource managementOceanographyEnvironmental protectionEcologyComputer scienceBiologyWater qualityGeologyEcosystem

Abstract

fetched live from OpenAlex

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.<br/>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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.204
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.047
GPT teacher head0.291
Teacher spread0.244 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations28
Published2023
Admission routes1
Has abstractyes

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