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Record W2561533006 · doi:10.1017/thg.2016.66

Abstracts From the ISTS Satellite Meeting June 20, 2016, Brisbane

2016· article· en· W2561533006 on OpenAlexaff

Bibliographic record

VenueTwin Research and Human Genetics · 2016
Typearticle
Languageen
FieldMedicine
TopicGlobal Health and Surgery
Canadian institutionsUniversity of TorontoMental Health Research Canada
Fundersnot available
KeywordsSatelliteContent (measure theory)Project commissioningPublishingHigh-content screeningComputer scienceEngineeringPolitical scienceChemistryMathematics

Abstract

fetched live from OpenAlex

A twin study consists of three different, but interrelated, research interests: studies 'of' twins, which investigate specificity of twin birth, twin relationship, and so on; studies 'by' twins, which provide a major methodology of behavior genetics to investigate how genes and environments work in human; and studies 'for' twins, which provide useful information for nurturing and fostering twin children in their families and societies.Main works in studies 'of' twins are on twin birth rate, zygosity diagnosis, and physical development.These findings also contribute to studies 'for' twins, which provide scientific references of social support for twin and their parents.Most of the active research region is study 'by' twins.There are two established twin research centers progressing: Osaka University Twin Research Center and Keio Twin Research Center.The former is medical oriented and focuses mainly on population from adolescent to elderly people.The latter is psychology oriented and focuses on population from infancy to adulthood.Currently, these two contrasted twin projects started to collaborate with each other to establish a nation-wide network for twin research in Japan.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.534
Threshold uncertainty score0.665

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0020.001
Scholarly communication0.0030.001
Open science0.0010.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.5340.327

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 head0.092
GPT teacher head0.394
Teacher spread0.302 · 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.

Study designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2016
Admission routes1
Has abstractyes

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