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Record W4399361566 · doi:10.1007/s10646-024-02754-y

An evaluation of fish and invertebrate mercury concentrations in the Caribbean Region

2024· article· en· W4399361566 on OpenAlexfundno aff
Linroy Christian, Mark E. H. Burton, Azad Mohammed, Wendy Nelson, Tahlia Ali Shah, Laël Bertide-Josiah, Helen G. Yurek, David C. Evers

Bibliographic record

VenueEcotoxicology · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicMercury impact and mitigation studies
Canadian institutionsnot available
FundersBeef Cattle Research Council
KeywordsMercury (programming language)InvertebrateTrophic levelFisheryFishingEcotoxicologyBiomonitoringGeographyEnvironmental scienceEcologyBiology

Abstract

fetched live from OpenAlex

Mercury is a ubiquitous pollutant of global concern but the threat of exposure is not homogenously distributed at local, regional, or global scales. The primary route of human exposure to mercury is through consumption of aquatic foods, which are culturally and economically important in the wider Caribbean Region, especially for Small Island Developing States (SIDS). We compiled more than 1600 samples of 108 unique species of fish and aquatic invertebrates collected between 2005 and 2023 from eleven countries or territories in the wider Caribbean Region. There was wide variability in total mercury concentrations with 55% of samples below the 0.23 µg/g wet weight (ww) guideline from the U.S. FDA/EPA (2022) for 2 or 3 weekly servings and 26% exceeding the 0.46 µg/g ww guideline consistent with adverse effects on human health from continual consumption, particularly for sensitive populations. Significant relationships were found between total mercury concentrations and taxonomic family, sampling country, fish length, and trophic level. The data analyzed here support the need for further sampling with concrete geospatial data to better understand patterns and mechanisms in mercury concentrations and allow for more informed decision making on the consumption of fish and invertebrates from the wider Caribbean Region as well as supporting efforts to evaluate the effectiveness of national, regional, and international mercury policies.

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)0.000
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 score0.353
Threshold uncertainty score0.451

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.046
GPT teacher head0.318
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 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

Citations6
Published2024
Admission routes1
Has abstractyes

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