The “plastic cycle”: a watershed‐scale model of plastic pools and fluxes
Bibliographic record
Abstract
Research on plastics in global ecosystems is rapidly evolving. Oceans have been the primary focus of studies to date, whereas rivers are generally considered little more than conduits of plastics to marine ecosystems. Within a watershed, however, plastics of all sizes are retained, transformed, and even extracted via freshwater use or litter cleanup. As such, plastic litter in terrestrial and freshwater ecosystems is an important but underappreciated component of global plastic pollution. To gain a holistic perspective, we developed a conceptual model that synthesizes all sources, fluxes, and fates for plastics in a watershed, including containment (ie disposed in landfill), non‐containment (ie persists as environmental pollution), mineralization, export to oceans, atmospheric interactions, and freshwater extraction. We used this model of the “plastic cycle” to illustrate which components have received the most scientific attention and to reveal overlooked pathways. Our main objective is for this framework to inform future research, offer a new perspective to adapt management across diverse waste governance scenarios, and improve global models of plastic litter.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".