Microplastic pollution in St. Lawrence River sediments
Bibliographic record
Abstract
Although widely detected in marine ecosystems, microplastic pollution has only recently been documented in freshwater environments, almost exclusively in surface waters. Here, we report microplastics (polyethylene microbeads, 0.40–2.16 mm diameter) in the sediments of the St. Lawrence River. We sampled 10 freshwater sites along a 320 km section from Lake St. Francis to Québec City by passing sediment collected from a benthic grab through a 500 μm sieve. Microbeads were discovered throughout this section, and their abundances varied by four orders of magnitude across sites. Median and mean (±1 SE) densities across sites were 52 microbeads·m −2 and 13 832 (±13 677) microbeads·m −2 , respectively. The highest site density was 1.4 × 10 5 microbeads·m −2 (or 10 3 microbeads·L −1 ), which is similar in magnitude to microplastic concentrations found in the world’s most contaminated marine sediments. Mean diameter of microbeads was smaller at sites receiving municipal or industrial effluent (0.70 ± 0.01 mm) than at non-effluent sites (0.98 ± 0.01 mm), perhaps suggesting differential origins. Given the prevalence and locally high densities of microplastics in St. Lawrence River sediments, their ingestion by benthivorous fishes and macroinvertebrates warrants investigation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".