An assessment of microplastics in fecal samples from polar bears (<i>Ursus maritimus</i>) in Canada's North
Bibliographic record
Abstract
We assessed the potential for plastic ingestion in polar bears ( Ursus maritimus (Phipps (1774))) using fecal analysis. Two method studies ensured our protocols could effectively recover and identify plastics in feces. First, microplastics (film, foam, or fragments) were intentionally introduced into a model organic matrix. Recovery rates (mean ± standard deviation) averaged 95.8 ± 14.7% ( n = 18) and were significantly affected by microplastic morphology but not digestion status. Second, microplastic fragments of polypropylene, polyethylene terephthalate, and polystyrene were intentionally introduced to polar bear feces. Recovery rates averaged 79.3 ± 21.6% ( n = 8) and Raman microscopy successfully identified all three polymers in 87.5% of samples. The main study then investigated the presence of microplastics in hunter-collected polar bear feces in the Canadian Arctic. Feces from the colons of hunted bears ( n = 15) and field scat ( n = 15) were collected through collaboration with Inuit communities. Polypropylene, polyethylene, and/or polyethylene terephthalate were detected in the feces of eight bears. Concentrations of microplastics in feces were, on average, less than 1 particle/g dry weight feces and at or near detection limits. Overall, this work suggests microplastic ingestion by Canadian polar bears may be low and demonstrates the utility of fecal sampling for community-based monitoring programs.
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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 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".