Microplastic contamination in Great Lakes fish
Bibliographic record
Abstract
Freshwater ecosystems, generally adjacent to human population and more contaminated relative to adjacent marine ecosystems, are vulnerable to microplastic contamination. We sampled 7 species of fish from Lake Ontario and Lake Superior and assessed their gastrointestinal (GI) tracts to quantify ingested microplastics and other anthropogenic particles. A subset of the microparticles were chemically analyzed to confirm polymer types and anthropogenic origins. We documented the highest concentration of microplastics and other anthropogenic microparticles ever reported in bony fish. We found 12,442 anthropogenic microparticles across 212 fish (8 species) from nearshore Lake Ontario, 943 across 50 fish (1 species) from Humber River, and 3094 across 119 fish (7 species) from Lake Superior. Fish from Lake Ontario had the greatest mean abundance of anthropogenic microparticles in their GI tracts (59 particles/fish [SD 104]), with up to 915 microparticles in a single fish. Fish from Lake Superior contained a mean [SD] of 26 [74] particles/fish, and fish from Humber River contained 19 [14] particles/fish. Most particles were microfibers. Overall, ≥90% of particles were anthropogenic, of which 35-59% were microplastics. Polyethylene (24%), polyethylene terephthalate (20%), and polypropylene (18%) were the most common microplastics. Ingestion of anthropogenic particles was significantly different among species within Lake Ontario (p < 0.05), and the abundance of anthropogenic particles increased as fish length increased in Lake Ontario (ρ = 0.62). Although we cannot extrapolate the concentration of microplastics in the water and sediments of these fish, the relatively high abundance of microplastics in the GI tracts of fish suggests environmental exposure may be above threshold concentrations for risk.
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 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) | 0.002 | 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 teacher head, 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".