Evaluating Canada's single-use plastic mitigation policies via brand audit and beach cleanup data to reduce plastic pollution
Bibliographic record
Abstract
Single-use plastics (SUPs) represent a major threat to marine environments and require proactive policies to reduce consumption and mismanagement. Many SUP management strategies exist to reduce SUP use and mitigate environmental impacts, including extended producer responsibility (EPR), deposit-return schemes, SUP bans or taxes, and public outreach and education. This study analyzed brand audit and beach cleanup data in four densely populated Canadian cities (Vancouver, Toronto, Montréal, Halifax) and a remote island (Sable Island) to determine efficacy of ongoing SUP mitigation measures. Cities were found to have similar litter type proportions, and six brands were found to disproportionally contribute to Canadian SUP litter, comprising 39% of branded litter collected. Results confirm that current Canadian SUP management appears to be insufficient to address leakage of SUPs into the environment. Recommendations to strengthen SUP management strategies and mitigate plastic pollution are recommended to improve future Canadian SUP reduction policies.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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 teacher head, 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".