The state of plastic pollution in Eastern Canada : From the Great Lakes to Newfoundland
Bibliographic record
Abstract
In 2024, Blue Organization (BO) released its first report on plastic pollution in Eastern Canada, aiming to raise awareness and foster collaboration against plastic waste. This five-year research, partially funded by Environment and Climate Change Canada (ECCC), involved 131 clean-ups across the Great Lakes, St. Lawrence River, estuary, and Gulf, collecting and analyzing 24 tons of waste. With the support of over 100 partners and 3,000 volunteers, BO also examined factors like consumer habits and waste management, providing insights into Eastern Canada’s plastic pollution.In spring 2024, BO launched a Portal on plastic pollution in Eastern Canada’s coastal zones. The Portal includes an interactive map, a detailed five-year report, and datasets hosted by the St. Lawrence Global Observatory and the Integrated Ocean Observing System. BO's work has revealed the pressing need to address macroplastic pollution on Canada’s shorelines, countering beliefs that plastic pollution is primarily an international issue. The impacts extend beyond ecosystems and human health to economic and social costs, even affecting remote, protected, and UNESCO-designated areas.After presenting findings at the Plastic Pollution Summit (INC-4) in Ottawa, BO began training over fifty regional organizations to apply its characterization protocol, encouraging collaborative data sharing. Partners include Fisheries and Oceans Canada, Parks Canada, regional ZIP committees, First Nations groups, and municipalities. BO’s work highlights the need for a coordinated national effort to address plastic waste, helping stakeholders identify pollution sources and implement effective reduction strategies.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.010 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".