Characterization of microplastics and anthropogenic fibers in surface waters of the North Saskatchewan River, Alberta, Canada
Bibliographic record
Abstract
Microplastics are globally ubiquitous contaminants, but quantitative data on their presence in freshwater environments are sparse. This study investigates the occurrence, composition, and spatial trends of microplastic contamination in the North Saskatchewan River flowing through Edmonton, Alberta, the fifth largest city in Canada. Surface water samples were collected from seven sites throughout the city, upstream and downstream of the city, and near potential point sources (i.e., a wastewater treatment plant). Samples were spiked with fluorescent microbeads as internal standards and extracted by wet peroxide oxidation and density floatation. Microplastics were found in all samples, ranging in concentration from 4.6 to 88.3 particles·m −3 (mean = 26.2 ± 18.4 particles·m −3 ). Fibers were the dominant morphology recovered, and most were of anthropogenic origin and chemically identified as dyed cotton or polyester by Raman microspectroscopy. The majority of fragments were identified as polyethylene or polypropylene. No upstream to downstream differences were found in concentration, size distribution, or morphological composition suggesting nonpoint sources of microplastics to the river. This study represents one of the first investigations into the occurrence of microplastics in the freshwater environment in western Canada and will provide a baseline for future studies.
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 | 0.002 | 0.003 |
| 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".