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Record W2018951556 · doi:10.1530/rep.1.00294

Molecular interactions of the aryl hydrocarbon receptor and its biological and toxicological relevance for reproduction

2005· review· en· W2018951556 on OpenAlexaff
Paola Pocar, Bernd Fischer, Thomas Klonisch, Sabine Hombach‐Klonisch

Bibliographic record

VenueReproduction · 2005
Typereview
Languageen
FieldEnvironmental Science
TopicToxic Organic Pollutants Impact
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsAryl hydrocarbon receptorAryl hydrocarbon receptor nuclear translocatorTranscription factorSignal transductionReproductionMediatorBiologyReceptorTranscription (linguistics)Cell biologyGeneticsGene

Abstract

fetched live from OpenAlex

The dioxin/aryl hydrocarbon receptor (AhR) is a ligand-activated transcription factor responsive to both natural and man-made environmental compounds. AhR and its nuclear partner ARNT are expressed in the female reproductive tract in a variety of species and several indications suggest that the AhR might play a pivotal role in the physiology of reproduction. Furthermore, it appears to be the mediator of most, if not all, the adverse effects on reproduction of a group of highly potent environmental pollutants collectively called aryl hydrocarbons (AHs), including the highly toxic compound 2,3,7,8-tetrachlor-odibenzo-p-dioxin (TCDD). Although a large body of recent literature has implicated AhR in multiple signal transduction pathways, the mechanisms of action resulting in a wide spectrum of effects on female reproduction are largely unknown. Here we summarize the major types of molecular cross-talks that have been identified for the AhR and linked cell signaling pathways and that are relevant for the understanding of the role of this transcription factor in female reproduction.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.048
GPT teacher head0.318
Teacher spread0.270 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations194
Published2005
Admission routes1
Has abstractyes

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