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Record W4307515312 · doi:10.1101/2022.10.28.514245

GENLIB: new function to simulate haplotype transmission in large complex genealogies

2022· preprint· en· W4307515312 on OpenAlexaff
Mohan Rakesh, Hélène Vézina, Catherine Laprise, Ellen E. Freeman, Kelly M. Burkett, Marie‐Hélène Roy‐Gagnon

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2022
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic Associations and Epidemiology
Canadian institutionsHôpital Maisonneuve-RosemontUniversité du Québec à ChicoutimiUniversity of Ottawa
Fundersnot available
KeywordsScripting languageHaplotypeR packageFunction (biology)Computer scienceTransmission (telecommunications)GenealogyBiologyEvolutionary biologyGeneticsProgramming languageHistoryGeneTelecommunications

Abstract

fetched live from OpenAlex

Abstract Summary Founder populations with deep genealogical data are well suited for investigating genetic variants contributing to diseases. Here, we present a new function added to the genealogical analysis R package GENLIB, which can simulate the transmission of haplotypes from founders to probands along very large and complex user-specified genealogies. Availability and implementation The new function is available in the latest version of the GENLIB package (v1.1.6), available on the CRAN repository and from https://github.com/R-GENLIB/GENLIB . Stand-alone scripts for analyzing the output of the function can be accessed at https://github.com/R-GENLIB/simuhaplo_scripts .

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)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Software · Consensus signal: none
Teacher disagreement score0.037
Threshold uncertainty score0.125

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0030.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0370.011

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 head0.024
GPT teacher head0.259
Teacher spread0.236 · 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 designSimulation or modeling
Domainnot available
GenreSoftware

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
Published2022
Admission routes1
Has abstractyes

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