Assessing the abundance, diversity and distribution of microplastics in the Upper St. Lawrence River
Bibliographic record
Abstract
Although microplastics are recognized as globally pervasive pollutants in fresh waters, their presence and fate in riverine environments are still poorly documented. Previous research demonstrated that microplastics in the form of polyethylene microbeads are abundant in the sediments of the St. Lawrence River; however, the extent to which the river is contaminated by microplastics (beads, fibres, fragments) in general and the factors that govern the distribution and abundance of such pollutants remain to be determined. In this thesis, I attempt to bridge this gap by quantifying the abundance and diversity of a broad range of microplastics in the sediments and surface waters and by relating these metrics to environmental variables in the St. Lawrence River. I sampled 21 sites spanning a land use gradient, including 10 wastewater effluent sites, along the fluvial corridor between Montreal and Quebec City. Microplastics were removed from sediments using an oil extraction protocol and enumerated under fluorescent microscopy. The mean concentration of microplastics across all sites was 832 (±150 SE) particles (range 62 to 7562 particles) per kg dry weight. I found that microplastic concentrations in the sediments can be predicted from a small selection of environmental variables. Particle characteristics, proximity to point sources, and environmental filters each play a role in explaining microplastic concentrations in the sediment. In water samples, mean concentrations of microplastics were 0.12±0.01 (SE) particles per litre upstream and 0.16±0.02 (SE) particles per litre downstream of wastewater effluents; but in only one case out of the ten wastewater effluents was the average microplastic concentration higher downstream of the effluent site. Overall, this is the first study to demonstrate empirically which environmental variables best explain the diversity, abundance and distribution of microplastic particles in riverine sediments. Furthermore, I present an interim protocol that can be used to detect microplastics with relatively high efficiency and accuracy, and that could be standardized for large-scale monitoring.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 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".