A comprehensive investigation of industrial plastic pellets on beaches across the Laurentian Great Lakes and the factors governing their distribution
Bibliographic record
Abstract
Industrial, pre-consumer pellets are a major type of plastics pollution found on shorelines worldwide. This study investigates the distribution and characteristics of plastic pellets accumulated on beaches of the Laurentian Great Lakes of North America and provides a “snapshot” of pellet distribution in a lake system that accounts for 21% of the world's freshwater reserves. We sampled pellets simultaneously from 10m2 quadrats on 66 beaches and characterized the 12,595 pellets collected (average of 19.1 pellets/m2). Forty-two beaches contained pellets and 86% of the pellets were found on three beaches: Rossport (Lake Superior), Baxter (Lake Huron), and Bronte (Lake Ontario). The number of pellets on each beach was compared with factors hypothesized to control their accumulation. In general, positive correlations were found between pellet abundance and watershed population, number of plastic-related industries, and proximity to a river mouth, although for Lake Superior, abundance was related to a train spill that took place over 10 years ago. Beach grain size appears to be related to pellet abundance, with very fine sand, fine sand and medium sand containing the greatest number of pellets. All pellets were visually characterized based on size, color, shape, weathering, and distinguishing traits. The predominant color was white, oblate shapes were most common, and the main distinguishing trait was a dimple. Most pellets showed little evidence of weathering, with the weathered samples mainly from Lakes Erie and Ontario. Lake Ontario pellets were the most varied, with 6/7 shapes, 35/40 colors, and 21/25 distinguishing traits, indicating a wider range of pellet sources compared to the other lakes. Polymer compositions were mainly polyethylene (PE) and polypropylene (PP). Our results will lead to increased recognition of regional pellet pollution in the Great Lakes watershed, thereby motivating change during their production, transport and use.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".