Evaluating community science sampling for microplastics in shore sediments of large river watersheds
Bibliographic record
Abstract
A community science project in the Ottawa River Watershed in Canada interacted with an existing volunteer base to collect sediment from 68 locations in the watershed over approximately 750 km. Ninety-one percent of the distributed kits were returned with 42 volunteers taking part in the project. After analysis, particle concentrations were relatively low compared to previous freshwater microplastic sediment research, with contributing factors including (but not limited to) the large size of the watershed, a lower population base compared to other researched freshwater watersheds, the relative size and discharge of the Ottawa River and the large seasonal fluxes experienced in the river basin. Utilising community science for sampling large freshwater watersheds demonstrated its advantages in the research, especially spatially. However, careful consideration to research design and implementation is essential for community science projects examining microplastics in freshwater sediments. Research teams should ensure they are responsible for strict quality assurance and quality control protocols, especially in the laboratory with sample preparation and processing. Nonetheless, community science is potentially an extremely useful approach for researchers to use for microplastic sampling projects over large spatial areas.
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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 | 0.012 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".