A quantitative risk assessment framework for mortality due to macroplastic ingestion in seabirds, marine mammals, and sea turtles
Bibliographic record
Abstract
Plastic ingestion has been documented in nearly 1,300 marine species, including every seabird family, marine mammal family, and sea turtle species. Acute mortality, due to obstruction, perforation, or torsion of the gastrointestinal (GI) tract, has been confirmed via necropsy in all three taxa; however, quantitative risk assessment for macroplastic ingestion poses unique challenges, with risk more dependent on probability of discrete events involving diverse plastic types rather than cumulative exposure models (e.g., LC 50 ). We model mortality risk associated with macroplastic ingestion in seabirds, marine mammals, and sea turtles, using data from more than 10,000 necropsies reported in the academic literature and stranding network databases. Employing an adapted Weibull Accelerated Failure Time model, we assess the relationship between the GI load (pieces and volume/animal length) of different plastic types—hard, soft, rubber, or fishing debris—and likelihood of plastic-induced mortality. Overall, 35% of seabirds, 12% of marine mammals, and 47% of sea turtles ingested plastic, and 1.6%, 0.7%, and 4.4% died from plastic, respectively. When modeling plastic together, a 90% chance of mortality was associated with 23 pieces (0.098 cm 3 /cm) in seabirds, 29 pieces (39.89 cm 3 /cm) in marine mammals, and 405 pieces (5.52 cm 3 /cm) in sea turtles (377 for juveniles). The plastic types that posed the greatest risks were rubber for seabirds, soft plastics and fishing debris for marine mammals, and hard and soft plastics for sea turtles. This research furthers scientific understanding of the likelihood of mortality from plastic ingestion and can inform monitoring, risk assessments, and management frameworks.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 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".