Plastics in the deep sea – A global estimate of the ocean floor reservoir
Bibliographic record
Abstract
The exponential increase in plastic production coupled with variable global waste management system efficiencies has resulted in large amounts of plastic waste entering the ocean every year. Although we know millions of tonnes of plastic have entered the oceans, we do not yet understand the patterns of its accumulation across space nor the drivers of these patterns. The deep ocean is expected to be a resting place, or reservoir, for plastic pollution. Here, we conducted a rigorous, systematic review of previously published datasets to synthesize our understanding of macroplastic pollution (> 5 mm) on the ocean floor. Using extracted data, we built predictive additive models to estimate the amount and distribution of plastic on the ocean floor. We built two models: one using data from remote operated vehicles (ROVs) and another using data from bottom trawls. Using the model built with ROV data, which was better-constrained, we estimate that 3 to 11 million metric tonnes (MMT) of plastic pollution resides on the ocean floor as of 2020. This is of similar magnitude to annual inputs from land and one to two orders of magnitude greater than what is predicted to be floating on the ocean surface. To improve future estimates and our understanding of global patterns, we provide recommendations for ocean floor monitoring of plastic pollution.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".