Bibliographic record
Abstract
There is growing interest in understanding sources and sinks of microplastics in freshwater environments. Microplastics are pieces of plastic that are <5 mm in size, where the main types are fibres, fragments, beads, and films. This project investigates microplastic occurrences and potential point sources within the urban North Saskatchewan River in Edmonton, Alberta. Water samples were collected with 53 μm plankton nets at 7 sites along this river in June 2017, upstream and downstream from Goldbar Wastewater Treatment Plant (WWTP), a likely point source. Following sampling, density flotation and wet peroxide oxidation were used to isolate any microplastics from co-collected material. After sieving the material into five size classes, visual microscopy revealed the presence of microplastics in the samples, consisting of a range of types and colours. The findings also suggest that Goldbar WWTP is likely not a point source for microplastic pollution in this urban river, contrasting similar studies that have generally found the opposite trend. Fragments appeared to be the primary plastic type recovered across all sites, yet differences in the proportions of microplastic types exist between larger and smaller size classes. For example, the 500 µm-1 mm size class contained more fibres upstream and more fragments downstream of Goldbar WWTP, and the 53-125 µm size class contained a higher proportion of beads compared to the 500 µm-1 mm size class. This study reveals that urban populations influence microplastic contamination in various ways, and the data will provide a valuable baseline for future monitoring studies. Faculty Mentor: Dr. Matthew Ross Discipline: Chemistry
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 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".