Factors Controlling the Distribution of Microplastic Particles in Benthic Sediment of the Thames River, Canada
Bibliographic record
Abstract
Investigations of microplastic abundances in freshwater environments have become more common in the past five years, but few studies concern the factors that control the distribution of microplastics in river systems. We sampled benthic sediment from 34 stations along the Thames River in Ontario, Canada, to determine the influence of land use, grain size, river morphology, and relative amount of organic debris on the distribution of microplastics. Once counted and characterized for shape, color, and size, microplastic abundances were normalized to the results from Fourier transform infrared spectroscopy on randomly selected particles. The results indicate that 78% of the fragments and only 33% of the fibers analyzed were plastic. The normalized microplastic quantities ranged from 6 to 2444 particles per kg of dry weight sediment (kg –1 dw). The greatest number of microplastics were identified in samples of the finest grain sizes and with the greatest amount of organic debris. Although there was no significant difference between microplastic abundances in urban versus rural locations, the average microplastic count for urban samples was greater (269 vs 195 kg –1 dw). In terms of river morphology, samples from along straight courses of the river contained fewer microplastics than samples from inner and outer bends. Overall abundances confirm how rivers contain a significant number of plastic particles and thus may be major conduits of microplastics to lake and ocean basins.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| 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".