An ecosystem-scale litter and microplastics monitoring plan under the Arctic Monitoring and Assessment Programme (AMAP)
Bibliographic record
Abstract
Lack of knowledge on levels and trends of litter and microplastics in the Arctic, is limiting our understanding of the sources, transport, fate, and effects is hampering global activities aimed at reducing litter and microplastics in the environment. To obtain a holistic view to managing litter and microplastics in the Arctic, we considered the current state of knowledge and methods for litter and microplastics monitoring in eleven environmental compartments representing the marine, freshwater, terrestrial, and atmospheric environments. Based on available harmonized methods, and existing data in the Arctic, we recommend prioritization of implementing litter and microplastics monitoring in the Arctic in four Priority 1 compartments—water, aquatic sediments, shorelines, and seabirds. One or several of these compartments should be monitored to provide benchmark data for litter and microplastics in the Arctic and, in the future, data on spatial and temporal trends. For the other environmental compartments, methods should be refined for future sources and surveillance monitoring, as well as monitoring of effects. Implementation of the monitoring activities should include community-based local components where possible. While organized as national and regional programs, monitoring of litter and microplastics in the Arctic should be coordinated, with a view to future pan-Arctic assessments.
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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 | 0.010 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".