Remote, but Not Isolated—Microplastics in the Sub-surface Waters of the Canadian Arctic Archipelago
Bibliographic record
Abstract
As the remote Canadian Arctic Archipelago (CAA) becomes increasingly connected to the rest of the world, there is an impetus to monitor the possible impact of this connectivity. The potential for increases in localised sources of plastic pollution resulting from the increasing navigability of the remote north has yet to be explored. Here we investigate microplastic samples which were collected aboard the Canadian Coast Guard Ship (CCGS) Amundsen in the summer of 2018 using the underway pump and a filtration system with Fourier transform infrared analysis. We investigate the character, abundance, and distribution of microplastic particles and fibres in the sub-surface waters across the Canadian Arctic and add to the limited dataset on plastic pollution in this region. We find that there are low concentrations of microplastics ranging from 0 to 0.282 n L–1 (average 0.031 ± 0.017 n L–1), comprising 71% polyester and acrylics. We investigate the size distribution of retained particles and fibres on three different filter mesh sizes connected to the underway pump (300, 100, and 50 μm) and find that a 300 μm mesh and a 100 μm mesh retain only 6 and 56%, respectively, of the total particles and fibres. We explore the role of shipping as a potential source of textile fibres and we suggest that future monitoring of plastics in the Canadian Arctic should use the current shipping fleet to monitor its own plastic footprint, utilising the underway pump and mesh sizes < 100 μm.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 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".