Uncovering the Mysteries of Retention Ponds: Comparing the Abundance and Type of Microplastics in Storm Water Ponds in London Ontario
Bibliographic record
Abstract
Microplastics are plastics <5 mm (Liu, 2019; Arthur et al., 2009). They are created in two ways: Intentionally or from the fragmentation of larger pieces of plastic (National Ocean Service, 2021). They can negatively impact human, wildlife and ecosystem health in many ways depending on the exposure, type, size, and shape of the microplastic (Campanale, 2020). Retention ponds are often created in neighborhoods to collect water in order to prevent flooding. They also often serve as habitat for wildlife. Sediment samples were collected in two ponds in London Ontario both dredged in 2016. Samples were processed in the lab and further analyzed under the microscope to isolate the microplastics. Results have not been determined yet, but the abundance and type of microplastic varies in both ponds. Plastic pollution in retention ponds should be considered more, as it poses a threat to human and ecosystem health.
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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 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".