Fate of microplastics in background headwater lake catchments using a particle balance approach
Bibliographic record
Abstract
Abstract Microplastics are pervasive contaminants of concern, yet their fate in headwater lakes, especially in background regions, remains relatively unknown. Further, few field studies have quantified the inputs, outputs, and pathways of microplastics at the catchment level. In this study, the flux of microplastics (MP) were quantified over a 12-month period for three background headwater lake catchments in Muskoka-Haliburton, Ontario, Canada. A microplastic particle balance approach was used, incorporating inputs from atmospheric deposition and stream inflows against outflows and sedimentation to lakes. Atmospheric deposition had the highest daily microplastic flux rate (3.84–8.04 MP/m2/day), suggesting that it is the dominant source of microplastics to lakes in background regions. Of the microplastics deposited on the catchment, 41–73% were retained in the terrestrial area. Furthermore, a large fraction of the microplastics entering the lake were retained (30–45%), suggesting that lakes are a reservoir for microplastics. The microplastic residence time for the study lakes ranged from 3.15 to 7.70 years. Fibres (> 60%) were the most common particle type, further, polyethylene terephthalate was the dominant polymer identified followed by polypropylene and polyamide.
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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) | 0.001 | 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".