Mathematically Modeling Plastic Pollution in the Great Lakes
Bibliographic record
Abstract
Mass estimates of plastic pollution based on open water surface samples differ by several orders of magnitude from what is predicted based on production and input rates to the world’s oceans and the Great Lakes. Researchers have proposed that this missing plastic may be located in other reservoirs, including beaches, nearshore water, the water column, or sediment. Studying plastic via sampling efforts is logistically challenging and time consuming. Models, which provide full spatial and temporal distributions, can help fill in the knowledge gaps that sampling efforts leave. Here we present the first three-dimensional modeling effort in the Great Lakes to incorporate vertical diffusion, non-neutrally buoyant particles, a functional biofouling model, and a beaching model. We focus on including mechanisms that could account for removal of plastic from open surface water. Our work suggests that plastic may be accumulating along beaches, with accumulation patterns depending on beach characteristics, current patterns, and near shore population. Additionally, we predict that plastic is accumulating in lake sediment, with the rate of deposition dependent on polymer density, lake depth, and the effects of biofouling. We estimate that there may be 381 tons of plastic in the water of Lake Erie, with the potential for another 2205-2382 tons deposited in the sediment each year in Lake Erie, and 1265-1348 tons per year deposited in Lake Ontario sediments. This work improves existing mass estimates in the water column and sediment deposition rates in Lake Erie and provides a first sediment deposition estimate for Lake Ontario. Together, the results indicate that plastic pollution is likely remaining within the Great Lakes system rather than exporting to the ocean.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".