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Published on: May 18, 2019
Assessing Seafloor Litter Classification: Lessons from an Online Proficiency Test
Eirin Husabø1, Briony Silburn2, Thomas Maes3
1GRID-Arendal, Teaterplassen 3, 4836, Arendal, Norway. eirin.husabo@grida.no.
Environmental Management
|August 4, 2026
Summary
Seafloor litter classification accuracy averaged 83% in a proficiency test assessing marine pollution monitoring guidelines. While generally good, some misclassifications may stem from the assessment method or guideline clarity, not user experience.
Area of Science:
- Marine Biology
- Environmental Science
- Pollution Monitoring
Background:
- Seafloor litter serves as a key indicator of marine pollution.
- Standardized categorization of seafloor litter is crucial for reliable pollution assessments.
- The International Council for Exploration of the Sea (ICES) provides guidelines for seafloor litter categorization.
Purpose of the Study:
- To evaluate the accuracy of seafloor litter classification using ICES guidelines.
- To assess the effectiveness of the ICES seafloor litter categorization manual and photo guide.
- To identify potential correlations between user experience and classification accuracy.
Main Methods:
- An online proficiency test was conducted where participants classified seafloor litter images.
- Classification was based on the ICES manual and associated photo guide.
- Statistical analysis explored relationships between participant experience and classification performance.
Main Results:
- Participants demonstrated a high average accuracy of 83% in classifying seafloor litter.
- No significant correlation was found between user experience and classification accuracy.
- Common misclassifications were observed, potentially due to the assessment format or guideline ambiguities.
Conclusions:
- The ICES seafloor litter categorization guidelines are largely effective, with users showing good classification ability.
- Further refinement of guidelines and assessment methods may reduce misclassification errors.
- Findings support quality assurance for marine pollution monitoring and understanding human error in datasets.
