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Record W4391171208 · doi:10.1002/ajp.23600

Lead exposure and its relationship with fecal cortisol levels in black howler monkeys (<i>Alouatta pigra</i>)

2024· article· en· W4391171208 on OpenAlexaff
María Fernanda Alvarez‐Velazquez, Maurício González-Jaurégui, Sergio Albino Miranda, Genoveva Rosano‐Ortega, Colin A. Chapman, Juan Carlos Serio‐Silva

Bibliographic record

VenueAmerican Journal of Primatology · 2024
Typearticle
Languageen
FieldPsychology
TopicPrimate Behavior and Ecology
Canadian institutionsVancouver Island University
FundersConsejo Nacional de Ciencia y Tecnología
KeywordsFecesWildlifeBiologyEcologyZoology

Abstract

fetched live from OpenAlex

Efficiently detecting early environmental threats to wildlife is vital for conservation. Beyond obvious dangers like habitat loss or deforestation, our study focuses on one of the most hazardous toxic metals for wildlife: lead (Pb). Pb is a widespread, cumulative, and insidious environmental pollutant that can trigger a wide range of physiological, biochemical, and behavioral disorders. In fact, Pb can cause permanent dysfunction of the major stress system, the hypothalamic pituitary adrenal (HPA) axis. We analyzed Pb and cortisol concentrations in fecal samples from Alouatta pigra in southern Mexico. Fecal samples were collected across six sites categorized as free-ranging (n = 65; conserved and disturbed) and from captive animals (n = 58). Additionally, we collected soil samples (n = 35). We found that Pb was present in 28% of fecal samples and 83% of soil samples. There was a positive relation between fecal and soil Pb levels, and fecal Pb concentration was negatively associated with cortisol levels. However, the claim of Pb being a direct interference with HPA axis requires further investigation. Given our findings, assessing wildlife exposure can be a valuable tool for understanding potential Pb exposure levels in the environment and its possible implications for human health. It can also serve as an early warning system of these consequences.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.752

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.036
GPT teacher head0.331
Teacher spread0.295 · 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 designObservational
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

Citations3
Published2024
Admission routes1
Has abstractyes

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