Degradation of Anthropogenic Debris in Stormwater Infrastructure of the Lake Ontario Watershed
Bibliographic record
Abstract
The accumulation of anthropogenic debris (AD) in the Great Lakes is a growing issue with largely unknown consequences for ecological and human health. To better constrain estimates of debris loading into Lake Ontario and elucidate the fate of AD accumulating upstream, an incubation experiment of the most commonly identified littered products was conducted in different stormwater infrastructure within the Lake Ontario Watershed. Chip bags, cigarette filters, and shopping bags were placed into storm drains, stormwater retention ponds, and along riparian zones of tributaries in December of 2022 and July of 2023 to test the spatial (type of stormwater infrastructure [SWI]) and temporal (season) impacts of AD entry into the environment. All Winter-deployed materials were aged for one, four, and 12 months; all Summer-deployed samples were aged for one month, with an additional set of cigarette filters collected after four months. Changes in material properties were evaluated using mass loss analysis for cigarette filters, Fourier transform infrared spectroscopy for chip and shopping bags, and optical microscopy, and tensile testing for all materials. Microbial community structure was assessed using 16S amplicon sequencing. Degradation varied by material, as cigarette filters rapidly degraded, especially during the summer deployment. Increased surface oxidation and changes to mechanical properties of shopping bags indicated degradation that varied across deployment times. Chip bags were resistant to degradation with no oxidation occurring, and differences in the mechanical properties between deployment seasons varied by SWI. Changes to the microbial community were driven by seasonal differences. Summer-deployed samples had site-specific communities. Changes to community structure over time were dependent on SWI. Identified microbial communities aligned with literature findings of bacteria associated with environmental plastics and some classes were known to degrade plastics.
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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 | 0.002 | 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".