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.
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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 | 0.030 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.008 | 0.003 |
| Research integrity | 0.012 | 0.004 |
| Insufficient payload (model declined to judge) | 0.029 | 0.014 |
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".