Input of Anthropogenic Debris Across a Rural to Urban Gradient in the Lake Ontario Watershed
Bibliographic record
Abstract
Anthropogenic debris (AD) is now ubiquitous across terrestrial, marine, and freshwater environments. While plastic is typically the dominant material in AD, non-plastic materials, including metal, glass, processed wood, and concrete, are a large part of the diverse debris entering and moving through the environment and may have similar environmental impacts. Current estimates of plastic entering the Great Lakes are coarse and none exist for other AD. This gap precludes development of source-based mitigation plans. This study evaluated debris quantity and composition in tributaries and storm sewers entering the Rochester Embayment of Lake Ontario. Using LittaTraps™ installed in storm drains, we evaluated the quantity and composition of debris entering the stormwater system in the City of Rochester and Town of Brighton, New York. The mass and composition of debris in LittaTrap™ samples were highly spatially variable, even among nearby sites. Patterns of input generally followed land use and land development, with high organic debris in residential areas, and high quantities of plastic and cigarette butts at some urban sites. In addition to tobacco-related debris, the most common products were associated with snack wrappers and other miscellaneous plastic debris. Macrodebris (>5 mm) transport in tributaries was very low, but higher during storms. Microdebris particles (< 5 mm) were not identified to polymer type, but showed some relationship with land use: suburban sites were generally higher than rural sites. Fibers were the most dominant microdebris by morphology. Our results illustrate the complexity of AD composition and highlight specific sources, especially in urban areas, where mitigation measures may be effective in reducing input and potential downstream harm.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".