A bellwether for microplastic in wetland catchments in the Great Lakes region
Bibliographic record
Abstract
This study is intended as a bellwether for the occurrence of microplastics (MPs) in Great Lakes wetlands. In 2020, sediment, surface water, and atmospheric deposition samples were collected from wetland catchments in or near five National Wildlife Refuges (NWRs) in the Great Lakes region: Horicon-WI, Seney-MI, Shiawassee-MI, Ottawa- OH, and Montezuma-NY. Sediment and surface water samples were taken from river, stream, and canal inflows and outflows to and from wetland areas. Atmospheric deposition samples were collected in carboys placed near established rain gauges. These sample sites were chosen as indicators of MP deposition into and out of the region’s wetland systems. MPs were extracted from each sample, enumerated, and categorized by particle morphology and polymer type. Average MP particle abundances in the sediment and surface water samples ranged from 344 to 538 particles kg-1 (dry weight) and 2–68 particles m-3, respectively. Atmospheric MP deposition ranged from 5.8 to 22.6 particles m-2 d-1. Fibers were the most abundant MP particle type found in each sample type (sediment, surface water, and atmospheric deposition), followed by fragments. These results suggest that input and retention of MPs are pervasive in the Great Lakes region and surrounding wetland areas.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".