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The Duality of Arsenic Metabolism: Impact on Human Health

2022· review· en· W4295754725 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueThe Annual Review of Pharmacology and Toxicology · 2022
Typereview
Languageen
FieldEnvironmental Science
TopicArsenic contamination and mitigation
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaAlberta Innovates
KeywordsArsenicDetoxification (alternative medicine)MetabolismMethylationArsenic poisoningMechanism (biology)ToxicityChemistryHuman healthArsenic toxicityBiotransformationEnvironmental chemistryToxicologyBiologyBiochemistryEnvironmental healthMedicineEnzymeOrganic chemistryPathologyPhilosophyGene

Abstract

fetched live from OpenAlex

Arsenic is a naturally occurring hazardous element that is environmentally ubiquitous in various chemical forms. Upon exposure, the human body initiates an elimination pathway of progressive methylation into relatively less bioreactive and more easily excretable pentavalent methylated forms. Given its association with decreasing the internal burden of arsenic with ensuing attenuation of its related toxicities, biomethylation has been applauded for decades as a pure route of arsenic detoxification. However, the emergence of detectable trivalent species with profound toxicity has opened a long-standing debate regarding whether arsenic methylation is a detoxifying or bioactivating mechanism. In this review, we approach the topic of arsenic metabolism from both perspectives to create a complete picture of its potential role in the mitigation or aggravation of various arsenic-related pathologies.

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)0.003
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.973
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
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 science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.052
GPT teacher head0.445
Teacher spread0.392 · 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