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Record W4407367279 · doi:10.3390/birds6010010

Birds as Bioindicators: Revealing the Widespread Impact of Microplastics

2025· article· en· W4407367279 on OpenAlexaboutno aff
Lara Carrasco, Eva Jiménez-Mora, Maria Jose Utrilla, Inés Téllez Pizarro, Laura Rico-San Román, Bárbara Martín‐Maldonado

Bibliographic record

VenueBirds · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicMicroplastics and Plastic Pollution
Canadian institutionsnot available
FundersUniversidad Europea de Madrid
KeywordsMicroplasticsBioindicatorEcologyEnvironmental scienceGeographyBiology

Abstract

fetched live from OpenAlex

The global crisis of plastic pollution, particularly involving microplastics (MPs) and nanoplastics (NPs), has profound ecological implications. Birds, serving as bioindicators, are especially susceptible to these pollutants. This systematic review synthesizes the current research on the presence, distribution, and impact of MPs and NPs on avian species, alongside advancements in detection methodologies. MPs and NPs have been identified in over 200 bird species across 46 families, encompassing several ecosystems, from Antarctica to Labrador, including Australia, China, and South Europe. Seabirds such as penguins, gulls, and shearwaters exhibit a high burden of MPs in tissues and feces due to fishing debris, while terrestrial species face contamination from urban and agricultural sources. Depending on their composition, MPs can cause gastrointestinal damage, oxidative stress, and bioaccumulation of toxic chemicals, particularly polyethylene and polypropylene. However, challenges in detection persist due to methodological inconsistencies, though advances in spectroscopy and flow cytometry offer improved accuracy. Addressing this pollution is vital for bird conservation and ecosystem health, requiring international collaboration and standardized research protocols.

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.

How this classification was reachedexpand

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.552
Threshold uncertainty score0.960

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.006
GPT teacher head0.255
Teacher spread0.248 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations30
Published2025
Admission routes1
Has abstractyes

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