Direct Microplastic Inputs from Wastewater Treatment Plants to the Laurentian Great Lakes
Bibliographic record
Abstract
Whilst wastewater treatment plants (WWTPs) have an inadvertent high microplastics retention capacity (typically >70%), they are also an important point source of pollutants to aquatic environments [1] . This allows WWTPs to serve as important control points to reduce microplastics pollution to receiving water bodies. In this study, we used a combination of spatially explicit total plastic waste generation [2] and facility data from the International Joint Commission [3] , to estimate the annual inputs of microplastics associated with direct wastewater discharges into each of the five Laurentian Great Lakes. The empirical calculations and plastic inputs take into account the population density and gross regional product bordering the lakes' coastlines, as well as the relative proportions of primary, secondary, and tertiary treatment of the discharging WWTPs [3] . These inputs are then tracked within a simple microplastics balance model. The model is used to assess the impacts of converting and improving wastewater treatment processes along the Great Lakes continuum. Going further we consider possible future scenarios, e.g., diminishing per capita microplastic emissions and projected population growth in the Great Lakes region.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".