Microplastic deposition velocity in streams follows patterns for naturally occurring allochthonous particles
Bibliographic record
Abstract
Abstract Accumulation of plastic litter is accelerating worldwide. Rivers are a source of microplastic (i.e., particles <5 mm) to oceans, but few measurements of microplastic retention in rivers exist. We adapted spiraling metrics used to measure particulate organic matter transport to quantify microplastic deposition using an outdoor experimental stream. We conducted replicated pulse releases of three common microplastics: polypropylene pellets, polystyrene fragments, and acrylic fibers, repeating measurements using particles with and without biofilms. Depositional velocity (v dep ; mm/s) patterns followed expectations based on density and biofilm ‘stickiness’, where v dep was highest for fragments, intermediate for fibers, and lowest for pellets, with biofilm colonization generally increasing v dep . Comparing microplastic v dep to values for natural particles (e.g., fine and coarse particulate organic matter) showed that particle diameter was positively related to v dep and negatively related to the ratio of v dep to settling velocity (i.e., sinking rate in standing water). Thus, microplastic v dep in rivers can be quantified with the same methods and follows the same patterns as natural particles. These data are the first measurements of microplastic deposition in rivers, and directly inform models of microplastic transport at the landscape scale, making a key contribution to research on the global ecology of plastic waste.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.000 | 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".