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Record W2466158804 · doi:10.1080/03601234.2016.1191888

Metal contents of marine turtle eggs (<i>Chelonia mydas; Lepidochelys olivacea</i>) from the tropical eastern pacific and the implications for human health

2016· article· en· W2466158804 on OpenAlexaff
David A. Ross, Héctor M. Guzmán, Vincent van Hinsberg, Catherine Potvin

Bibliographic record

VenueJournal of Environmental Science and Health Part B · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicTurtle Biology and Conservation
Canadian institutionsMcGill University
FundersEuropean Food Safety Authority
KeywordsHazard quotientMercury (programming language)CadmiumArsenicFisheryTurtle (robot)BiologySea turtleHealth riskHealth hazardAnimal scienceEcologyToxicologyContaminationChemistryEnvironmental healthMedicine

Abstract

fetched live from OpenAlex

Concentrations of eight elements were measured in Chelonia mydas and Lepidochelys olivacea eggs collected along the Pacific coast of Panama. Manganese (Mn), iron (Fe), copper (Cu), zinc (Zn), arsenic (As), cadmium (Cd), and mercury (Hg) concentrations were similar to previous reports of these species from around the world, while lead (Pb) was lower than previous reports. Cd posed the highest health risk to people who regularly eat the eggs, with average consumption rates leading to target hazard quotients (THQ) of up to 0.35 ± 0.15. Our conclusions indicate that current turtle egg consumption in isolated, coastal Pacific communities may pose a health concern for young children, and that youth and young adults should limit their consumption of turtle eggs to reduce their total intake of nonessential metals.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.042
GPT teacher head0.309
Teacher spread0.267 · 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 source (direct Gemma or distilled Codex), 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

Citations19
Published2016
Admission routes1
Has abstractyes

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