A systematic review of state-of-the-art technologies for monitoring plastic seafloor litter
Bibliographic record
Abstract
Plastic litter has been widely documented in our oceans, leading to growing worldwide concerns regarding its potential impact on the marine environment. A large proportion of this plastic accumulates at the bottom of the ocean, resulting in a need to monitor and quantify seafloor litter. Seafloor litter monitoring is mostly performed using benthic beam trawls, which have several limitations and environmental implications. New innovative ways to document and address seafloor litter are therefore necessary and requested by the United Nations Sustainable Development Goal 14 (SDG 14.1.1b), the Oslo Paris Convention (OSPAR) and the International Council for the Exploration of the Sea (ICES). This systematic review gives an overview of the state-of-the-art of 14 current underwater technologies that are eligible for future in situ detection of plastic litter on the seafloor based on 101 publications. A set of objectives and a Technology Readiness Level (TRL) scale were used to benchmark the technologies and revealed that the most suitable system is often very scenario-specific and, therefore, demands investments in more than one specific group of technologies. A decision tool was established to determine the most suitable technique for a range of different situations. This review indicates that most of these technologies are currently at low-middle TRLs, requiring several more development, testing and commercialization steps before they can be applied effectively in marine field conditions. However, these technologies, alone or in combination, have the potential to contribute to the establishment of more robust global environmental indicators and monitoring programs for plastic pollution.
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 | 0.006 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".