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Record W6892297501 · doi:10.5061/dryad.7g5s4

Data from: A genetic locus for paranoia

2017· dataset· en· W6892297501 on OpenAlexaff

Bibliographic record

VenueData Archiving and Networked Services (DANS) · 2017
Typedataset
Languageen
Field
Topic
Canadian institutionsUniversity of AlbertaSimon Fraser University
Fundersnot available
KeywordsParanoiaPsychosisLocus (genetics)PopulationGenetic dataGenetic variantsSchizophrenia (object-oriented programming)Genotyping

Abstract

fetched live from OpenAlex

The psychological effects of brain-expressed imprinted genes in humans are virtually unknown. Prader-Willi syndrome (PWS) is a neurogenetic condition mediated by genomic imprinting, which involves high rates of psychosis characterized by hallucinations and paranoia, as well as autism. Altered expression of two brain-expressed imprinted genes, MAGEL2 and NDN, mediates a suite of PWS-related phenotypes, including behavior, from studies of mice. We phenotyped a large population of typical individuals for schizophrenia-spectrum and autism-spectrum traits, and genotyped them for the single-nucleotide polymorphism rs850807, which is putatively functional and linked with MAGEL2 and NDN. Genetic variation in rs850807 was strongly and exclusively associated with the Ideas of Reference subscale of the schizophrenia spectrum, which is best typified as paranoia. These findings provide a single-locus genetic model for analyzing the neurological and psychological bases of paranoid thinking, and implicate imprinted genes, and genomic conflicts, in human mentalistic thought.

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)0.001
metaresearch head score (Gemma)0.008
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: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.079
Threshold uncertainty score0.264

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.004
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0020.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0790.027

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 head0.078
GPT teacher head0.344
Teacher spread0.266 · 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
GenreDataset

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

Citations2
Published2017
Admission routes1
Has abstractyes

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