The power of multi-matrix monitoring in the Pan-Arctic region: plastics in water and sediment
Bibliographic record
Abstract
Litter and microplastic assessments are being carried out worldwide. Arctic ecosystems are no exception and plastic pollution is high on the Arctic Council's agenda. Water and sediment have been identified as two of the priority compartments for monitoring plastics under the Arctic Monitoring and Assessment Programme (AMAP). Recommendations for monitoring both compartments are presented in this publication. Alone, such samples can provide information on presence, fate, and potential impacts to ecosystems. Together, the quantification of microplastics in sediment and water from the same region produce a three-dimensional picture of plastics, not only a snapshot of floating or buoyant plastics in the surface water or water column but also a picture of the plastics reaching the shoreline or benthic sediments, in lakes, rivers, and the ocean. Assessment methodologies must be adapted to the ecosystems of interest to generate reliable data. In its current form, published data on plastic pollution in the Arctic is sporadic and collected using a wide spectrum of methods which limits the extent to which data can be compared. A harmonised and coordinated effort is needed to gather data on plastic pollution for the Pan-Arctic. Such information will aid in identifying priority regions and focusing mitigation efforts.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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 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".