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Record W4391738039 · doi:10.1101/2024.02.02.578687

Fine-scale genetic structure and rare variant frequencies

2024· preprint· en· W4391738039 on OpenAlexafffund
Laurence Gagnon, Claudia Moreau, Catherine Laprise, Simon Girard

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2024
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEvolution and Genetic Dynamics
Canadian institutionsUniversité LavalCentre Intégré Universitaire de Santé et de Services Sociaux du Saguenay–Lac-Saint-JeanUniversité du Québec à Chicoutimi
FundersCanadian Institutes of Health ResearchAlliance de recherche numérique du Canada
KeywordsFounder effectEvolutionary biologyHomogeneity (statistics)Scale (ratio)PopulationGenetic structureBiologyComputational biologyComputer scienceData scienceGenetic variationGeographyGeneticsMachine learningCartographyDemographySociologyGeneGenotypeHaplotype

Abstract

fetched live from OpenAlex

Abstract In response to the current challenge in genetic studies to make new associations, we advocate for a shift toward leveraging population fine-scale structure. Our exploration brings to light distinct fine-structure within populations having undergone a founder effect, challenging the prior perception of homogeneity. This underscores that smaller, but well-defined cohorts, demonstrate an important increase in rare variant frequencies, offering a promising avenue for new genetic variants’ discovery.

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)0.003
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.005
GPT teacher head0.199
Teacher spread0.194 · 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 designObservational
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

Citations1
Published2024
Admission routes2
Has abstractyes

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