Factors controlling the abundance, distribution, and composition of macroplastic and microplastic particles in tributary, beach, and benthic sediments of Lake Erie, Ontario
Bibliographic record
Abstract
Plastic pollution in the Laurentian Great Lakes is becoming a significant environmental concern with the threats of species entanglement, adsorption of toxins such as endocrine-disrupting chemicals (EDCs) and persistent organic molecules (POPs), and subsequent ingestion. Lake Erie tributary (by Petite Ponar), beach (by Split Spoon sampler), and benthic sediments (by Shipek grabs) were collected and evaluated for microplastic particles (0.5 mm) (by quadrats and transects). These results were mapped using ArcGIS software to show distribution and abundance in regards to quaternary watershed population density and plastic industrial plants, manufacturers, and distributors. Tributaries in urban areas were more abundant in microplastics than in more rural tributaries. At beaches, backshores were more abundant in microplastics than in the foreshore, likely due to natural beach dynamics of sediment accumulation. The greatest abundance of microplastics was found in the Western Basin of Lake Erie, where the Detroit River drains into the lake. Quaternary watersheds borderingLake Eriewith higher population densities were most abundant in microplastic and macroplastic pollution. Macroplastics were most abundant at beaches in highly populated areas, and macroplastics were least abundant at beaches that were part of conservation areas. A random selection of microplastic fragments and microbeads were analysed using Nicolet Almega Dispersive Raman Spectroscopy and NXR FT-Raman Spectroscopy to determine types of plastics. Polyethylene was the most common microplastic observed among this sample selection. Overall, high population density around the sampling locations correlated to a higher abundance in plastic debris. Conservation areas had the lowest abundance of plastic debris; therefore, employing conservation area environmental practices could be beneficial to reducing plastic debris at other locations along Lake Erie.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".