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Record W1999759833 · doi:10.3200/aeoh.61.5.232-238

New Evidence on the Effects of Tea on Mercury Metabolism in Humans

2006· article· en· W1999759833 on OpenAlexaff
René Canuel, Sylvie Boucher de Grosbois, Marc Lucotte, Laura Atikessé, Catherine Larose, Isabelle Rhéault

Bibliographic record

VenueArchives of Environmental & Occupational Health · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicMercury impact and mitigation studies
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsMethylmercuryMercury (programming language)Fish <Actinopterygii>Fish consumptionEnvironmental chemistryToxicologyChemistryPhysiologyAnimal scienceBioaccumulationBiologyFishery

Abstract

fetched live from OpenAlex

The authors present the results of an experiment in which they explored the role of tea in human metabolic processing of methylmercury (MeHg) from fish consumption. The experiment involved 50 scientists from the Collaborative Mercury Research Network (COMERN) who agreed to eat fish for 2 daily meals for 3 consecutive days. Half of the participants also drank 6 cups of tea daily, starting a week before and continuing through the experiment. The authors calculated the total amount of MeHg that each participant ingested from (1) the measured mercury (Hg) level in fish and (2) the quantity of fish eaten, and compared it with the total increases of Hg and MeHg levels in participants' blood. Results indicated that the control group metabolized roughly 100% of the available fish MeHg, whereas the tea-exposed group showed blood levels of MeHg at more than 40% than that available in the fish provided, suggesting that an external MeHg pool supplied part of the measured blood MeHg increase. The authors conclude that tea may accelerate the enterohepatic MeHg cycle and contribute to a temporary bioamplification of MeHg in the bloodstream.

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 score0.164
Threshold uncertainty score0.472

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.018
GPT teacher head0.291
Teacher spread0.273 · 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

Citations27
Published2006
Admission routes1
Has abstractyes

Explore more

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