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Record W4408997042 · doi:10.1111/azo.12550

Contaminant levels and their effects on the American continent chelonian: A systematic review

2025· review· en· W4408997042 on OpenAlexaboutno aff
Lucas Maia Garcês, Adriano Teixeira de Oliveira

Bibliographic record

VenueActa Zoologica · 2025
Typereview
Languageen
FieldEnvironmental Science
TopicMercury impact and mitigation studies
Canadian institutionsnot available
Fundersnot available
KeywordsBiologyZoology

Abstract

fetched live from OpenAlex

Abstract Excessive metal pollution from anthropogenic activities like mining and industrial waste discharge has increasingly impacted freshwater ecosystems. Freshwater chelonians (Testudines) are bioindicators of environmental pollution because of their longevity, ecological diversity, and trophic positioning. This systematic review assessed toxic metal bioaccumulation in freshwater turtles across the American continent, focusing on mercury (Hg), a global public health and ecological concern. A comprehensive search on PubMed, Scopus and Web of Science identified 620 papers, of which 32 met the inclusion criteria. The United States and Brazil (93.6%) contributed the most data, with Chelydra serpentina and Podocnemis expansa frequently studied, followed by Canada (4.26%) and Colombia (2.13%). Hg was the predominant contaminant (75% of studies), with the highest concentrations observed in hatchlings and keratinized tissues, such as carapace and claws. Bioaccumulation patterns varied by region, species and life stage, emphasizing physiological and reproductive impacts. This review highlights the need for noninvasive sampling methods and long‐term monitoring to guide conservation strategies and assess ecosystem and turtle health. It also highlights the significant lack of data in South America and the lack of studies on juveniles and eggs. Freshwater chelonians remain essential for understanding contamination dynamics and mitigating environmental degradation in aquatic habitats.

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)0.001
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: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.479
Threshold uncertainty score0.736

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.000
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)0.0000.000

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 head0.034
GPT teacher head0.306
Teacher spread0.272 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations5
Published2025
Admission routes1
Has abstractyes

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