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Record W4293403537 · doi:10.1139/er-2022-0071

Biomonitoring of heavy metals and metalloids with wild mammals in the Iberian Peninsula: a systematic review

2022· review· en· W4293403537 on OpenAlexvenueno aff
Catarina Jota Baptista, Fernanda Seixas, José M. Gonzalo‐Orden, Paula A. Oliveira

Bibliographic record

VenueEnvironmental Reviews · 2022
Typereview
Languageen
FieldEnvironmental Science
TopicMercury impact and mitigation studies
Canadian institutionsnot available
Fundersnot available
KeywordsBiomonitoringHabitatTrophic levelEcologyFood chainPeninsulaHeavy metalsIndicator speciesAquatic ecosystemSentinel speciesBiologyEnvironmental chemistryChemistry

Abstract

fetched live from OpenAlex

Trace elements (including heavy metals) can negatively affect the environment and the health of living beings. Biomonitoring is a transdisciplinary tool to evaluate this pollution type and its respective consequences in ecosystems, food chains, and webs. This review used systematic methods to identify published literature on biomonitoring of heavy metal(loid)s using wild mammals on the Iberian Peninsula. A total of 30 different mammalian species (30/141) were included in 62 Iberian biomonitoring studies: 22 species from terrestrial habitats and 8 from aquatic habitats. Carnivores (including piscivores) were the most represented in both habitat types (7/22 in terrestrial; 8/8 in aquatic). Most studies used more than one tissue (2.8 ± 1.3), with a preference for the liver and kidney. Cd was the most determined metal, measured in 45% of the biomonitoring studies analysed, highlighting its potential health impact on mammals. Further research is crucial to provide more information on mammalian species' susceptibility to this One Health problem, and to cover more habitats, trophic chains, and (or) geographical areas.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.870
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0040.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.055
GPT teacher head0.316
Teacher spread0.260 · 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.

Study designNot applicable
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

Citations14
Published2022
Admission routes1
Has abstractyes

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