The utility of monitoring snow for microplastics in the Arctic: a pilot study from Iqaluktuuttiaq, Nunavut
Bibliographic record
Abstract
Plastic pollution, including microplastics (<5 mm) has been identified as an emerging contaminant of Arctic concern and has been observed in wildlife, water, sediment, air, and snow. Because snow is relatively easy to sample and process for microplastics, it may be a useful compartment to monitor to assess patterns of microplastic contamination in polar regions. Microplastics can enter the Arctic through both long-range transport pathways and from local sources. By sampling snow across spatial scales, and multiple distances from local communities, researchers can explore local and distant sources of microplastics, thereby informing management strategies. With this in mind, we aimed to quantify mass concentrations of microplastics in snow samples collected north-east of Iqaluktuuttiaq, Nunavut. We sampled five sites in a transect moving away from town and quantified microplastics using Pyrolysis/gas chromatography with mass spectrometry. We found microplastics at every location, but patterns along the transect were unclear. We observed differences in polymer types at sampling sites closer to the community compared to sites further away suggesting the presence of local inputs. Overall, we highlight the use of snow as a local monitoring tool to assess contamination and sources of microplastics in the Arctic to inform future long-term monitoring programs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".