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Behavioral epigenetics

2011· article· en· W2314829979 on OpenAlexaff
Barry M. Lester, Edward Z. Tronick, Eric J. Nestler, Ted Abel, Barry E. Kosofsky, Christopher W. Kuzawa, Carmen J. Marsit, Ian Maze, Michael J. Meaney, Lisa M. Monteggia, Johannes M. H. M. Reul, David Skuse, J. David Sweatt, Marcelo A. Wood

Bibliographic record

VenueAnnals of the New York Academy of Sciences · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicEarly Childhood Education and Development
Canadian institutionsMcGill University
FundersSchool of Medicine, University of California, IrvineEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentJanssen PharmaceuticalsNational Institute of Child Health and Human DevelopmentUniversity of California, IrvineJanssen Scientific AffairsNational Institute of Mental HealthUniversity of Massachusetts BostonAstraZenecaNational Institute on Drug AbuseBristol-Myers SquibbMarch of Dimes FoundationBrown UniversityNational Institutes of HealthMassachusetts Life Sciences Center
KeywordsEpigeneticsNormativeResearch centerPsychologyPsychopathologyGerontologyLibrary scienceMedicinePolitical scienceBiologyPsychiatryGeneticsComputer scienceLaw

Abstract

fetched live from OpenAlex

Sponsored by the New York Academy of Sciences, the Warren Alpert Medical School of Brown University and the University of Massachusetts Boston, "Behavioral Epigenetics" was held on October 29-30, 2010 at the University of Massachusetts Boston Campus Center, Boston, Massachusetts. This meeting featured speakers and panel discussions exploring the emerging field of behavioral epigenetics, from basic biochemical and cellular mechanisms to the epigenetic modulation of normative development, developmental disorders, and psychopathology. This report provides an overview of the research presented by leading scientists and lively discussion about the future of investigation at the behavioral epigenetic level.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.062
Threshold uncertainty score0.208

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0620.005

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 head0.270
GPT teacher head0.409
Teacher spread0.139 · 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

Citations115
Published2011
Admission routes1
Has abstractyes

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