The Fate of Microplastics in Rural Headwater Lake Catchments
Bibliographic record
Abstract
In this study, the fluxes of microplastics (mp) were quantified during a 12-month period for three rural headwater lake catchments in Muskoka-Haliburton, south-central Ontario, Canada. A novel catchment particle balance approach was used, incorporating inputs from atmospheric deposition and stream inflows against lake outflow and sedimentation. This approach provides the first reported observation-based estimates of microplastic residence time in freshwater lakes. Atmospheric deposition had the highest daily microplastic flux (3.95–8.09 mp/m 2 /day), compared to the inflow streams (2.21–2.34 mp/m 2 /day), suggesting that it is the dominant source of microplastics to rural regions. Approximately 44–71% of the deposited microplastics were retained in the terrestrial catchments and 30–49% of the microplastics in the stream inflows were retained in the study lakes. Given that output fluxes ranged from 0.72–3.76 mp/m 2 /day in the sediment and 1.18–1.66 mp/m 2 /day in the lake outflows, the microplastic residence time was estimated to be between 3 and 12 years, suggesting that lakes are an important reservoir for microplastics. Fibers were the dominant shape in atmospheric deposition, streamwater, and lake water; however, in lake sediment, there was a higher proportion of fragments. Across all media, poly(ethylene terephthalate) was the dominant polymer identified (23%).
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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 | 0.001 | 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".