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Record W2184066828 · doi:10.1515/iupac.81.0001

Immunological Effects of Mercury

2016· dataset· en· W2184066828 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.

Bibliographic record

VenueIUPAC Standards Online · 2016
Typedataset
Languageen
FieldDentistry
TopicOral Health Pathology and Treatment
Canadian institutionsCanada Research ChairsUniversity of Toronto
Fundersnot available
KeywordsMercury (programming language)Immune systemAntibodyAutoimmune diseaseImmunologyAutoantibodyAutoimmunityAntigenImmunosuppressionChemistryBiology

Abstract

fetched live from OpenAlex

Various chemical species of mercury differ considerably with regard to their route of absorption and their distribution in the body, yet many of them and their metabolites exhibit high-affinity binding to sulfanyl groups of proteins. Among all metals, mercury appears to have the most diverse effects on the immune system. Depending on the animal species and experimental conditions, mercury compounds may cause immunosuppression or immunostimulation, autoimmune reactions, or hypersensitivity. Mercury-sensitive strains of rats and mice are often used as model organisms to study the time course and events in autoimmunity. Within about 14 days after onset of oral mercury(II) exposure, levels of immunoglobulins E and G (IgE and IgG) increase, including autoantibodies to biomolecules such as laminin and fibrillarin. Antigen-antibody complexes are formed and are the cause of subsequent autoimmune diseases of blood vessels and organs. Mercury may induce local mercury hypersensitivity in humans, but the evidence for a role of mercury in autoimmune disease of humans is at best weak. Models for the immune effects of mercury are presented on the basis of current knowledge.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.011
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.015
GPT teacher head0.432
Teacher spread0.417 · 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