Effets fondateurs et variabilité génétique au Québec
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".