Towards a management strategy for microplastic pollution in the Laurentian Great Lakes - Monitoring (Part 1)
Bibliographic record
Abstract
Plastic contamination extends across all Great Lakes ecosystems, including in wildlife, with the potential for risk based on laboratory experiments and risk assessment. Due to widespread contamination, and based on evidence suggesting measurable risk, it is time for policy-makers to develop and implement monitoring programs to guide management . Here, we discuss the need for a monitoring strategy with clear guidelines. We synthesize the research that has been published across the Great Lakes, reporting on contamination, regions that have been the focus of study, and the methods used across matrices. Based on our findings, we suggest how research may inform guidelines and next steps – especially if microplastics are to be considered as a Toxic Chemicals sub-indicator under the Great Lakes Water Quality Agreement. Future monitoring, using standard and/or harmonized protocols for sampling and analysis, should build baselines across the basin and begin tracking how contamination changes to assess the health of the Great Lakes, to inform source-reduction, and to measure the effectiveness of policies aimed at reducing emissions of plastics to freshwater.
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".