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Record W2139399279 · doi:10.1139/a08-001

Selenium and mercury in organisms: Interactions and mechanisms

2008· article· en· W2139399279 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueEnvironmental Reviews · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicMercury impact and mitigation studies
Canadian institutionsLaurentian University
Fundersnot available
KeywordsMercury (programming language)BioaccumulationSeleniumEnvironmental chemistryMethylmercuryAntagonismChemistryToxicityEcotoxicologyBiochemistry

Abstract

fetched live from OpenAlex

This paper reviews the growing literature dealing with the antagonistic effect of selenium (Se) compounds on the toxicity of mercury (Hg) compounds in higher animals and organisms present in the aquatic environment. It covers both laboratory and field studies and summarizes the possible mechanisms that explain the protective action of Se compounds on mercuric mercury (Hg 2+ ) and methylmercury (CH 3 Hg + ) toxicity. The review is subdivided according to the molecular form of Hg and the organisms in which the antagonism has been studied. Many authors suggest that the protective effect of selenite on the toxicity of Hg 2+ in mammals is due mainly to the in vivo formation of mercuric selenide (HgSe), a stable and biologically inert complex. The detection of HgSe has been confirmed in several studies in support of this mechanism. Possible mechanisms that may be involved in the antagonism between Se compounds and CH 3 Hg + in mammals and aquatic organisms are also presented. The possibility of adding Se compounds to contaminated lakes and reservoirs as a remediation technique to limit the bioaccumulation of Hg 2+ and CH 3 Hg + is critically discussed.

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.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.446
Threshold uncertainty score1.000

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)0.0010.001

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 head0.023
GPT teacher head0.252
Teacher spread0.229 · 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