An assessment of microplastics in fecal samples from polar bears (Ursus maritimus) 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 preliminary studies were conducted to ensure our methods could effectively recover and identify plastics in polar bear feces. In the first study, in which microplastics (film, foam, or fragments) were intentionally introduced into an 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. In the second study, in which microplastics of three polymers were intentionally introduced to polar bear feces, recovery rates averaged 79.3 ± 21.6% (n = 8), and Raman spectroscopy successfully identified all polymers in 87.5% of samples. The main study then investigated whether microplastics are present in polar bear feces in the Canadian Arctic. Colon feces (n = 15) and scat (n = 15) were collected from 30 polar bears through collaboration with Indigenous communities. Microplastics (polypropylene, polyethylene, and/or polyethylene terephthalate) were found in fecal samples from eight polar bears, although concentrations were low (<1 particle/g dry weight feces, on average). This study provides new information on plastics in Canadian bears and suggests fecal sampling can be utilized in community-based 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 | 0.000 | 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 | 0.000 |
| 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".