Review: Monitoring to conservation: The science–policy nexus of plastics and seabirds — R1/PR6
Bibliographic record
Abstract
Seabirds have been the messengers of marine plastics pollution since the 1950s, not long after plastics began to be commercially manufactured. In the decades since, a number of multilateral agreements have emerged to address marine plastics pollution that have been informed by research and monitoring on plastic ingestion in seabirds. Seabirds continue to serve as effective monitors for plastics pollution in the oceans, and increasingly of the chemical contamination from the marine environment as plastic additives and chemicals can adsorb and accumulate in seabirds’ tissues. Plastics pollution has far-reaching ecological impacts, but the motivation for addressing the issue has escalated rapidly at the international level. Seabirds are also the most globally threatened group of birds and require concerted conservation actions to mitigate population declines from multiple pressures. However, most policy mechanisms focus on the monitoring and mitigation of anthropogenically induced stressors, using seabird data, and often fail to include mechanisms to conserve the messengers. In this review, we discuss how research on the impacts of plastics on seabirds is used to inform policy and highlight the competing interests of monitoring and conservation that emerge from this approach. Finally, we discuss policy opportunities to ensure seabirds can continue to be the indicators of ocean health and simultaneously achieve conservation goals.
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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 | 0.005 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.027 | 0.008 |
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".