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Record W2164692271 · doi:10.1002/ajmg.b.32132

Selected rapporteur summaries from the XX world congress of psychiatric genetics, Hamburg, Germany, october 14–18, 2012

2013· article· en· W2164692271 on OpenAlexaff
Heike Anderson‐Schmidt, Olga Beltcheva, Mariko D. Brandon, Enda M. Byrne, Eric Chater‐Diehl, Laramie E. Duncan, Suzanne Gonzalez, Eilís Hannon, Katri Kantojärvi, Iordanis Karagiannidis, Mark Z. Kos, Eszter Kótyuk, Benjamin I. Laufer, Katarzyna Mantha, Nathaniel W. McGregor, Sandra Meier, Vanessa Nieratschker, Helen Spiers, Alessio Squassina, Geeta A. Thakur, Yash Tiwari, Biju Viswanath, Michael Way, Cybele C. P. Wong, Anne O’Shea, Lynn E. DeLisi

Bibliographic record

VenueAmerican Journal of Medical Genetics Part B Neuropsychiatric Genetics · 2013
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetics and Neurodevelopmental Disorders
Canadian institutionsMcGill UniversityDouglas Mental Health University InstituteWestern University
FundersNational Institute on Drug AbuseNational Institute of Mental HealthNational Institute on Alcohol Abuse and Alcoholism
KeywordsPsychiatric geneticsPsychiatryLibrary scienceMedicinePolitical scienceSchizophrenia (object-oriented programming)

Abstract

fetched live from OpenAlex

The XXth World Congress of Psychiatric Genetics (WCPG), sponsored by The International Society of Psychiatric Genetics (ISPG) took place in Hamburg, Germany on October 14-18, 2012. Approximately 600 participants gathered to discuss the latest findings in this rapidly advancing field. The following report was written by student travel awardees. Each was assigned sessions as rapporteurs. This manuscript represents topics covered in most, but not all, oral presentations during the conference, and some of the major notable new findings reported at this 2012 WCPG.

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)0.003
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: Review · Consensus signal: none
Teacher disagreement score0.122
Threshold uncertainty score0.409

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.1220.050

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 head0.009
GPT teacher head0.243
Teacher spread0.234 · 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

Citations9
Published2013
Admission routes1
Has abstractyes

Explore more

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