Abundance and composition of microplastics in surface waters and sediments of five south-central Lake Ontario tributaries
Bibliographic record
Abstract
More than 10,000 metric tons of plastic enter the Great Lakes every year. Most of this is microplastic, tiny plastic particles less than five millimeters in length or diameter. Microplastic pollution is a growing environmental concern in the Great Lakes where these particles can affect aquatic life as well as humans if ingested. To better understand potential sources of microplastics in Lake Ontario, we surveyed microplastic concentration in five tributaries within the south-central Lake Ontario basin in both surface waters and sediments. We analyzed the microplastic morphologies and polymer types and compared the results to three sites in nearshore south-central Lake Ontario. Tributaries surface samples had significantly higher microplastic concentrations (4.9 microplastics/m³) compared to lake sites (1.3 microplastics/m³). Tributary sediments had an average concentration of 0.16 microplastics/g dry weight. Fibers were the most common particle morphologies in tributary surface waters (49%) and sediments (52%) while fragments were the most common morphology found in lake surface waters (73%). These morphologies are harder for aquatic life to pass if ingested and are more likely to remain in the gut, leading to potential health issues and bioaccumulation in the food web. Polyethylene (recycling types two and four) and Other polymers (recycling type 7) accounted for over 90% of microplastics captured. Tributaries are important sources of microplastic pollution in south-central Lake Ontario and should be included in plastic prevention strategies. Furthermore, knowing the most prevalent morphologies and polymers may help to pinpoint sources of plastic and contribute to targeted prevention
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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) | 0.001 | 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 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".