Baseline Analysis of Microplastics in Remote Boreal Lake Sediments at the Experimental Lakes Area
Bibliographic record
Abstract
Microplastics have become a contaminant of emerging concern because they have been characterized as a ubiquitous and persistent pollutant. At < 5mm in size, these particles have proven to generate a multitude of physical and chemical effects in organisms at varying trophic levels. However, there have been few baseline studies examining long-term microplastic pollution trends in remote boreal lakes. In the present study, we sought to determine the difference in microplastic abundances between recently deposited surface (depth = 0-5 cm) sediments and pre-industrially deposited deep (depth = 30-35 cm) sediments in Lake 378 and Lake 373 at the International Institute for Sustainable Development-Experimental Lakes Area in Northwestern Ontario. We collected three replicate sediment cores per lake and sectioned cores to collect recent and pre-industrial sediment samples from each. Laboratory analysis was then conducted to extract particles from sediment samples, before counting, measuring and visually categorizing them by shape and color. Mean particle abundances in recently deposited sediments were not significantly different from pre-industrial sediments in Lake 378 (p = 0.151) and Lake 373 (p = 0.376). There were also no significant differences between the mean sizes of particles in recently and pre-industrially deposited sediments for both Lake 378 (p = 0.961) and Lake 373 (p = 1.000). In Lake 378 and Lake 373 the highest percent abundances of recovered particle shapes in recent sediments were fragments and fibers, while pre-industrial sediments were largely composed of fibers. No notable differences were observed in the colors of the recovered particles from recent and pre-industrial sediments for both lakes. Based on the results of this study, recently deposited sediments do not show significant differences in abundance and particle characterization from pre-industrially deposited sediment. Further investigation through the chemical analysis of recovered particles is needed to confirm these findings.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".