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Record W4376133600 · doi:10.1002/bies.202300019

Can you remember silence? Epigenetic memory and reversibility as a site of intervention

2023· article· en· W4376133600 on OpenAlexafffund
Stephanie Lloyd, Pierre-Éric Lutz, Chani Bonventre

Bibliographic record

VenueBioEssays · 2023
Typearticle
Languageen
FieldPsychology
TopicIdentity, Memory, and Therapy
Canadian institutionsUniversité LavalDouglas Mental Health University Institute
FundersCanadian Institutes of Health ResearchCentre National de la Recherche ScientifiqueFondation de FranceUniversité de StrasbourgFondation FyssenAgence Nationale de la RechercheAmerican Foundation for Suicide Prevention
KeywordsSilenceEpigeneticsTraitIntervention (counseling)PsychologyBiologyGeneticsPsychiatryAestheticsArtGeneComputer science

Abstract

fetched live from OpenAlex

Just over 20 years ago, molecular biologists Leonie Ringrose and Renato Paro published an article with a provocative title, "Remembering Silence", in BioEssays. The article focused on how epigenetic elements could return to their silent state, operationally defined as their epigenetic status before their modulation by experimental or environmental factors. Though Ringrose and Paro's article was on fruit flies and factors affecting embryological growth, the article asked a question of considerable importance to rapidly expanding research in neuroepigenetics on the correlation between trauma and neuropsychiatric risk: If you experience a traumatic event and, as a result, acquire an epigenetic trait that is considered pathological, can you free yourself of that trait? Ultimately, we are interested in how a return to silence is envisioned in neuroepigenetics research, how interventions purported to bring about that silence might function, and what this might mean for people who live in the aftermath of trauma.

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)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.008
Scholarly communication0.0020.003
Open science0.0010.003
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0060.001

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 head0.026
GPT teacher head0.323
Teacher spread0.297 · 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 designTheoretical or conceptual
Domainnot available
GenreEmpirical

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

Citations5
Published2023
Admission routes2
Has abstractyes

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