Categorization of plastic debris on sixty-six beaches of the Laurentian Great Lakes, North America
Bibliographic record
Abstract
Abstract The Laurentian Great Lakes system is a major global sink for plastic debris. An area of 10 m2 on each of sixty-six Great Lakes beaches was sampled for large micro-, meso- and macroplastic items. A total of 21 592 plastic items were collected and categorized. Pre-production plastic pellets were the most abundant debris type, accounting for 58.3% of the total count. The remaining 42.7% of the debris items are the focus of this study. Detailed, multi-step characterization was performed with the plastics being categorized using physical identification, known usage, and Fourier transform infrared spectroscopy (FTIR). Values of 805.5 items m−2 at Baxter Beach in Sarnia, Ontario, Canada, and 688.1 items m−2 at Bronte Beach in Oakville, Ontario, Canada are the highest of all sampling locations. Sampling sites on only three beaches contained no plastic debris: Bay City in Michigan, U.S.A., Presque Isle State Park in Erie, Pennsylvania, U.S.A. and Pebble Beach in Marathon, Ontario, Canada. The plastic items sampled were mainly large microplastics (68.4% of total) with a total of 1477.5 items m−2, followed by mesoplastics (27.3% of total) with 598.8 items m−2, and macroplastics (4.3% of total) with 91.9 items m−2. By mass, the microplastic fraction accounted for 25.61 g m2 (14.1%), the mesoplastic for 47.06 g m2 (25.9%), and macroplastic for 109.3 g m2 (60.1%). A total of 3004 items were determined as specific polymers based on physical properties, known polymer usage, Resin Identification Code, and FTIR. A total of 1227 plastic items (40.8% of total) were identified as expanded polystyrene. The 49 most common items, excluding pellets, were scored using a matrix scoring technique to determine their potential general origin. It was determined that these items mostly originated from shoreline and urban sources, whereas pellets originated from the plastics industry.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| 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".