A First Attempt at Modeling the Global Reservoir of Plastic in Biota: A Case Study With Sea Turtles
Bibliographic record
Abstract
Abstract Marine wildlife ingest plastic, making them a reservoir for plastic debris. To date, we lack estimates of how much plastic resides within marine animals globally, making it difficult to reconcile the fate of plastic in the global ocean. To help fill this knowledge gap, we collected data on amount of plastic debris found in sea turtles necropsy studies from the scientific literature up until 1 January 2020. We aimed to use this data ( n = 60), along with predictor variables, in a regression model to quantify how much plastic resides within sea turtles globally. We found that geographic (absolute latitude), socio‐economic (income of country), and ecological (species name) indicators significantly correlate with how much plastic is found in sea turtle gastrointestinal tracts. Our multispecies model suggests that leatherback turtles ( Dermochelys coriacea ) contain the most plastic in their gastrointestinal tracts, and loggerhead turtles ( Caretta caretta ) contain the least. This presents one of the first attempts to understand which sea turtle species has the highest propensity for plastic ingestion. Due to limited data, we were only able to estimate the amount of plastics that reside in female green turtles ( Chelonia mydas ) globally for a snapshot in time. Here, we provide the first estimate of a global marine animal reservoir of plastic– we estimate that at any given time, female green turtles carry 5.6–6.4 tonnes of plastic in their gastrointestinal tracts. We discuss future research directions to help reduce the uncertainty surrounding this estimate and fill in the gaps for other species.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".