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Record W1987205065 · doi:10.1051/medsci/200723111008

Effets fondateurs et variabilité génétique au Québec

2007· article· fr· W1987205065 on OpenAlexaffabout
Claudia Moreau, Hélène Vézina, Damian Labuda

Bibliographic record

Venuemédecine/sciences · 2007
Typearticle
Languagefr
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic and phenotypic traits in livestock
Canadian institutionsUniversité du Québec à ChicoutimiUniversité de MontréalCentre Hospitalier Universitaire Sainte-Justine
Fundersnot available
KeywordsHumanitiesPhilosophy

Abstract

fetched live from OpenAlex

Knowledge of the genetic population structure lies at the heart of mapping studies aiming genes responsible for Mendelian and complex traits. The Quebec population, which is of mostly French descent, is considered an excellent model for such genetic epidemiological endeavours because it is a young founder population. Yet, the assessment of the founder effect has relied mostly on the observed distribution of monogenic diseases and on the analysis of the underlying mutations with investigations focusing on the Saguenay region. To eliminate this clinical bias and to obtain a more complete image of the genetic diversity, different regional populations of Quebec were investigated by analysing neutral markers that represent maternal, paternal and X chromosome lineages. Results indicate that Quebec does not appear more homogeneous nor significantly different from European populations. However, a series of regional founder effects, particularly visible at the level of rare variants, are observed. These effects can be explained by the successive migrations of descendants of the first immigrants from the initial sites of settlement towards the outer regions. Depending on the number of founders and their diversity, as well as on the degree of isolation and the magnitude of the interbreeding with the neighbouring or local populations, such as Amerindians or later migrants, the consequences of these regional founder effects are more or less detectable in the contemporary population.

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 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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.474
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.016
GPT teacher head0.292
Teacher spread0.276 · 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 teacher head, not a consensus.

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

Citations15
Published2007
Admission routes2
Has abstractyes

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