The Global Plastic Cycle: Elucidating the Sources and Fate of Plastic Pollution in the Environment
Bibliographic record
Abstract
Every minute, a garbage truck’s worth of plastic pollution enters the oceans. These plastic particles are transported through the ocean and accumulate in characteristic reservoirs or resting places, which is part of the “Global Plastic Cycle” (Chapter 1). To date, we do not have a good estimate of plastic emissions into the oceans. Moreover, reservoirs have not been quantified, impeding our ability to conduct a proper mass balance of plastic in the environment. The objective of my doctoral research is to fill these gaps to better characterize the Global Plastic Cycle. First, I aimed to develop a method to better quantify emissions of plastic pollution entering the environment, and eventually the ocean (Chapter 2). Second, I aimed to quantify major reservoirs of plastic pollution in the marine environment including the ocean floor (Chapter 3), coastlines (Chapter 4), and marine animal (Chapter 5) reservoirs. For my first research chapter (Chapter 2), I introduced a framework for tracking emissions of plastic pollution called an emissions inventory of plastic pollution, to help quantify how much plastic enters the environment and inform effective mitigation of plastic pollution at its source. We applied this framework to the City of Toronto, and estimated that 3,531-3,852 tonnes of plastic pollution are emitted from the city annually. For Chapters 3-5, I built regression models using data on plastic abundances in the environment to predict the total mass of plastic contained within major reservoirs of plastic pollution. I found that the ocean floor, coastlines, and sea turtle reservoirs contain 3-11 million metric tonnes, 6-7 kilotonnes, and 7-9 tonnes of plastic pollution, respectively, respectively. In my conclusion, I summarize the studies of reservoirs to date, and find that estimates within each reservoir span many orders of magnitude, showing that the quality of data is less than adequate for a proper mass balance to be conducted. My doctoral research has generated a tool for formal accounting of plastic emissions, it has helped to improve our understanding of the fate of plastic pollution after it enters the ocean, and it explores future work for elucidating the fate of plastic pollution in the marine environment.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".