Understanding microplastic pollution on shorelines with citizens and technology
Bibliographic record
Abstract
Plastic pollution is a rapidly growing problem, that requires both data and engagement of the public to address effectively. Everyday people can help scientists improve the scale and pace at which microplastics data is gathered. The understanding of smaller microplastics is very limited because citizen science programs have often targeted the larger particles on beaches. In partnership with Environment and Climate Change Canada, a pilot study involving 50 volunteers was conducted to improve knowledge on microplastic debris contamination on shorelines and to develop tools for standardized particle analysis by citizen science programs. Over six months, volunteers collected monthly samples of visible microplastics (0.5 mm to 5 mm) in sand, seawater and stormwater (down to 60 µm) at ten different beach sites across the Greater Victoria Region (British Columbia, Canada). Water samples were collected using newly developed sampling kits designed to prevent contamination, to isolate microplastics for analysis in the lab. Quantification and morphological properties of visible microplastics were obtained using an AI-powered imaging technology coupled to Raman spectroscopy analysis to identify material polymer and inform on putative sources. The collected data sheds light on the seasonal variation and inter-site variability of microplastics and the role of various factors such as recreational shoreline use, influences of urban discharge, beach morphology, and hydrological conditions in microplastic shoreline contamination. The use of the standardized imaging technology enabled high-throughput physical analysis resulting in robust and consistent data collection. The results of this study will be used to raise awareness about microplastic pollution in Canada via a digital campaign, and to inspire citizens to participate in data collection programs and solutions to plastic pollution.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".