Conceptual model of global plants entrapping plastics
Bibliographic record
Abstract
Aquatic plants, seagrasses, macrophytes, mangroves, and riparian vegetation are responsible for some of the most important ecosystem services provided on the Earth. Given their role in trapping plastics along rivers, we propose a new ecosystem service of plastic entrapment by global plants. Although research started recently to study vegetation trapping plastics, little is known about the global patterns of plastic retention and remobilization by vegetation through different habitats. Given those gaps, we synthesize global data on plastic entrapment in plants providing a conceptual model to describe processes for plastic retention by vegetation. Our results demonstrate how vegetation has a pivotal role in entrapping plastics across spatial and temporal scales, finding the higher density of plastics on plants rather than in the adjacent water area. Furthermore, we proposed a conceptual model (i.e., Plant Plastic Pathway) of plants entrapping plastics, highlighting spatial and temporal scales of plastic retention and release processes in different habitats. Thus, we anticipate our conceptual model to be a starting point for more sophisticated future studies, putting effort into looking at plastic–vegetation dynamics. Our conceptual model may have a crucial effect if applied to plastic hotspot area detection with clean-up and mitigation actions in riverine ecosystems.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".