Investigating the potential uptake of microplastic-derived carbon into a boreal lake food web using carbon-13 labelled plastic
Bibliographic record
Abstract
Microplastics degrade slowly over time, leaching carbon (C) that could be subsequently incorporated into aquatic food webs. Current estimates of microplastic degradation vary, and little is known about microplastic-derived C fate under natural environmental conditions. To investigate whether microplastics leach C that is subsequently incorporated into aquatic food webs, we added isotopically enriched microplastics to Lake 378 at the Experimental Lakes Area in Ontario, Canada. In an ∼1100 L limnocorral (in situ open-bottom enclosure), we added 99% 13C-labelled polystyrene (8–216 µm in longest dimension) at a nominal concentration of 3268 particles/L. A second limnocorral without microplastics served as a negative control. Monthly measurements of δ13C-DIC and δ13C-DOC in filtered water revealed no detectable leaching of 13C from the plastic. Compound-specific isotope analysis of δ13C in amino acids of bulk plankton and periphyton revealed a slight (0.5‰) enrichment in 13C, within the range of natural variability for these organisms. Under the natural conditions of temperate oligotrophic lakes, degradation of microplastics is likely a very slow process that was not possible to detect in this 4-month experiment. Future studies should focus on assessing degradation of microplastics under realistic field scenarios to improve estimates of degradation pathways and associated time scales.
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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 | 0.000 | 0.000 |
| 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) | 0.000 | 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 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".