A first assessment of microplastics and other anthropogenic particles in Hudson Bay and the surrounding eastern Canadian Arctic waters of Nunavut
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Microplastics are a globally ubiquitous contaminant, invading the most remote regions, including the Arctic. To date, our understanding of the distribution and sources of microplastics in the Arctic is limited but growing. This study aims to advance our understanding of microplastics in the Arctic. Surface water, zooplankton, sediment, and snow samples were collected from Hudson Bay to north Baffin Bay onboard the CCGS Amundsen from July to August 2017. Samples were examined for microplastics, which were chemically identified via Raman spectroscopy for surface water and zooplankton and Fourier transform infrared spectroscopy for sediment. We found that 90% of surface water and zooplankton samples, and 85% of sediment samples, contained microplastics or other anthropogenic particles. Mean anthropogenic particle concentrations, which includes microplastics, were 0.22 ± 0.23 (per litre) for surface water, 3.51 ± 4.00 (per gram) for zooplankton, and 1.94 ± 4.12 (per gram) for sediment. These concentrations were not related to the human populations upstream, suggesting that microplastic contamination in the Arctic is from long-range transport. Overall, this study highlights the presence of microplastics across the eastern Canadian Arctic, in multiple media, and offers evidence of long-range transport via ocean and atmospheric currents. Further research is needed to better understand sources, distribution, and effects to Arctic ecosystems.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 it