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Record W2017985956 · doi:10.1093/toxsci/kfm096

An Aryl Hydrocarbon Receptor Odyssey to the Shores of Toxicology: The Deichmann Lecture, International Congress of Toxicology-XI

2007· article· en· W2017985956 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

VenueToxicological Sciences · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicToxic Organic Pollutants Impact
Canadian institutionsCanada Research ChairsUniversity of Toronto
Fundersnot available
KeywordsAryl hydrocarbon receptorToxicityXenobioticEnvironmental toxicologyBiologyToxicologyGenePharmacologyGeneticsMedicineBiochemistryTranscription factorInternal medicine

Abstract

fetched live from OpenAlex

The science of toxicology is devoted, in large part, to understanding mechanisms of toxicity so that we can more accurately assess the risk posed by exposure to xenobiotic agents and, perhaps, intervene in the toxicologic process to mitigate harm. Dioxin-like chemicals continue to be of great concern as environmental toxicants. About 30 years ago the aryl hydrocarbon receptor (AHR) was discovered as a specific binding site for 2,3,7,8-tetrachlorodibenzo-p-dioxin. This giant step led to our current view that essentially all toxic effects of dioxins are AHR-mediated. The AHR serves as the archetype for understanding toxicity mediated by other soluble receptors. The fact that toxicity is receptor-mediated has important implications, especially for dose-response relationships. In laboratory animals genetic differences in AHR gene structure lead to profound differences in responsiveness to dioxin-like chemicals. Humans, however, exhibit relatively few AHR polymorphisms and these seem to exert only modest effects on downstream events. Dioxin toxicity is fundamentally due to AHR-mediated dysregulation of gene expression. Our current challenging goal is to determine which dysregulated genes underlie specific forms of dioxin toxicity. Mapping AHR-mediated gene expression in a variety of biological systems may help explain why dramatic differences in susceptibility to dioxin toxicity exist among laboratory species and why humans appear to be relatively resistant to adverse effects of dioxins.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.641
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.003
Scholarly communication0.0000.000
Open science0.0030.001
Research integrity0.0000.000
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 head0.021
GPT teacher head0.304
Teacher spread0.282 · 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