Status and future recommendations for recording and monitoring litter on the Arctic seafloor
Bibliographic record
Abstract
Marine litter in the Arctic Basin is influenced by transport from Atlantic and Pacific waters. This highlights the need for harmonization of guidelines across regions. Monitoring can be used to assess temporal and spatial trends but can also be used to assess if environmental objectives are reached, for example, to evaluate the effectiveness of mitigation measures. Seafloor monitoring by trawling needs substantial resources and specific sampling strategies to be sufficiently robust to demonstrate changes over time. Observation and visual evaluation in shallow and deep waters using towed camera systems, remotely operated underwater vehicles, and submersibles are well suited for the Arctic environment. The use of imagery still needs to be adjusted through automation and image analyses, including deep learning approaches and data management, but will also serve to monitor areas with a rocky seafloor. We recommend developing a monitoring plan for seafloor litter by selecting representative sites for visual inspection that cover different depths and substrata in marine landscapes, and recording the litter collected or observed across all forms of seafloor sampling or imaging. We need better coverage and knowledge of status of seafloor litter for the whole Arctic and recommend initiatives to be taken for regions where such knowledge is lacking.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".