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Record W2154667256 · doi:10.1017/thg.2014.67

International Network of Twin Registries (INTR): Building a Platform for International Collaboration

2014· article· en· W2154667256 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueTwin Research and Human Genetics · 2014
Typearticle
Languageen
FieldMedicine
TopicEthics in Clinical Research
Canadian institutionsMcGill UniversityMcGill University Health Centre
FundersNational Research Foundation of KoreaAcademy of FinlandNational Research Foundation
KeywordsHarmonizationData sharingWorld Wide WebComputer scienceData scienceMedicine

Abstract

fetched live from OpenAlex

The International Network of Twin Registries (INTR) aims to foster scientific collaboration and promote twin research on a global scale by working to expand the resources of twin registries around the world and make them available to researchers who adhere to established guidelines for international collaboration. Our vision is to create an unprecedented scientific network of twin registries that will advance knowledge in ways that are impossible for individual registries, and includes the harmonization of data. INTR will also promote a broad range of activities, including the development of a website, formulation of data harmonization protocols, creation of a library of software tools for twin studies, design of a search engine to identify research partners, establishment of searchable inventories of data and biospecimens, development of templates for informed consent and data sharing, organization of symposia at International Society of Twin Studies conferences, support for scholar exchanges, and writing grant proposals.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.011
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.299
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.418
GPT teacher head0.582
Teacher spread0.164 · 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