Microplastics in lakes and rivers: an issue of emerging significance to limnology
Bibliographic record
Abstract
Microplastics, i.e., plastic particles in the size range of planktonic organisms, have been found in the water columns and sediments of lakes and rivers globally. The number and mass of plastic particles drifting through a river can exceed those of living organisms such as zooplankton and fish larvae. In freshwater sediments, concentrations of microplastics reach the same magnitude as in the world’s most contaminated marine sediments. Such particles are derived from a unique biogeochemical cycle that ultimately influences productivity, biodiversity, and ecosystem functioning. Furthermore, microplastics act as vectors of toxic substances to invertebrates, fishes, herpetofauna, and waterfowl. We contend that the concentration of this distinct particle component is an ecologically significant parameter of inland waterbodies because of its ubiquity, environmental persistence, and interactions with key ecological processes. No environmental field survey that has searched for microplastics has yet failed to detect their presence. Standardized limnological protocols are needed to compare spatio-temporal variation in the concentration of microplastics within and across watersheds. Data obtained from such protocols would facilitate environmental monitoring and inform policy for managing plastic waste; furthermore, they would enable more accurate modeling of contaminant cycling and the development of a global plastic budget that identifies sources, distribution and circulation pathways, reservoir size, and retention times.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".