Microplastics Distribution within Western Arctic Seawater and Sea Ice
Bibliographic record
Abstract
Microplastic pollution has emerged as a global environmental concern, exhibiting wide distribution within marine ecosystems, including the Arctic Ocean. Limited Arctic microplastic data exist from beached plastics, seabed sediments, floating plastics, and sea ice. However, no studies have examined microplastics in the sea ice of the Canadian Arctic Archipelago and Tallurutiup Imanga National Marine Conservation Area, and few have explored Arctic marginal seas' water column. The majority of the microplastic data originates from the Eurasian Arctic, with limited data available from other regions of the Arctic Ocean. This study presents data from two distinct campaigns in the Canadian Arctic Archipelago and Western Arctic marginal seas in 2019 and 2020. These campaigns involved sampling from different regions and matrices, making direct comparisons inappropriate. The study's primary objective is to provide insights into the spatial and vertical distribution of microplastics. The results reveal elevated microplastic concentrations within the upper 50 m of the water column and significant accumulation in the sea ice, providing evidence to support the designation of sea ice as a microplastic sink. Surface seawater exhibits a gradient of microplastic counts, decreasing from the Chukchi Sea towards the Beaufort Sea. Polyvinyl chloride polymer (~60%) dominated microplastic composition in both sea ice and seawater. This study highlights the need for further investigations in this region to enhance our understanding of microplastic sources, distribution, and transport.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".