An Evaluation of Microplastics in Lac Hertel Sediment Over Time
Bibliographic record
Abstract
Background: Microplastics, defined as plastic smaller than 5 mm, are pervasive in both marine and freshwater ecosystems. Humans, zooplankton, and fish have been shown to ingest microplastics, which could have detrimental health impacts. Consequently, this project investigated the question: are there microplastics in the sediment of Lac Hertel, located in the Mont Saint Hilaire Biosphere Reserve in Quebec, and if so, how has the amount of microplastics changed over time?Methods: One sediment core was obtained from the centre of the lake and one was obtained from the edge near the mesocosm dock. Next, one section from the top, middle, and bottom of each core was collected. Afterwards, the microplastics were extracted from the sediments, counted with a dissecting microscope under regular light, and a subset of fragments were tested with a hot needle to confirm that they were plastic.Results: A generalized linear model indicated that the number of microplastics in our samples increased significantly over time and that the sediment samples from the mesocosm dock had significantly fewer microplastics than the lake’s centre. Similarly, a Pearson correlation test revealed that an increasing sediment depth had a significantly negative relationship with the number of microplastics at the lake’s centre. However, another Pearson correlation test determined that this trend was not reflected at the mesocosm dock, potentially because of sediment focusing.Limitations: Due to resource and time constraints, we had a small sample size, only analyzed microplastics larger than 250 µm, and counted microplastics instead of weighing them.Conclusion: Our results suggest that there has been a significant increase in microplastics in Lac Hertel sediment over time. Ultimately, our results emphasize the need to mitigate plastic pollution.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".