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 m 2 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 m 2 (14.1%), the mesoplastic for 47.06 g m 2 (25.9%), and macroplastic for 109.3 g m 2 (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 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 | 0.000 | 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 | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".