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Record W4375862382 · doi:10.1017/plc.2023.2

Monitoring to conservation: The science–policy nexus of plastics and seabirds

2023· article· en· W4375862382 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueCambridge Prisms Plastics · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicMicroplastics and Plastic Pollution
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPlastic pollutionSeabirdNexus (standard)Threatened speciesEnvironmental planningCitizen scienceMarine conservationPorpoisePopulationEnvironmental protectionEnvironmental resource managementEnvironmental sciencePollutionBusinessEcologyEngineeringBiologyEnvironmental healthHabitatComputer science

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.575
Threshold uncertainty score0.539

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.017
GPT teacher head0.249
Teacher spread0.232 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it