High Levels of Microplastics in the Arctic Sea Ice Alga <i>Melosira arctica</i>, a Vector to Ice-Associated and Benthic Food Webs
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Plastic pollution has become ubiquitous with very high quantities detected even in ecosystems as remote as Arctic sea ice and deep-sea sediments. Ice algae growing underneath sea ice are released upon melting and can form fast-sinking aggregates. In this pilot study, we sampled and analyzed the ice algae Melosira arctica and ambient sea water from three locations in the Fram Strait to assess their microplastic content and potential as a temporary sink and pathway to the deep seafloor. Analysis by μ-Raman and fluorescence microscopy detected microplastics (≥2.2 μm) in all samples at concentrations ranging from 1.3 to 5.7 × 10 4 microplastics (MP) m –3 in ice algae and from 1.4 to 4.5 × 10 3 MP m –3 in sea water, indicating magnitude higher concentrations in algae. On average, 94% of the total microplastic particles were identified as 10 μm or smaller in size and comprised 16 polymer types without a clear dominance. The high concentrations of microplastics found in our pilot study suggest that M. arctica could trap microplastics from melting ice and ambient sea water. The algae appear to be a temporary sink and could act as a key vector to food webs near the sea surface and on the deep seafloor, to which its fast-sinking aggregates could facilitate an important mechanism of 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 | 0.001 | 0.000 |
| 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".