The effective family size of immigrant founders predicts their long-term demographic outcome: from Québec settlers to their 20th-century descendants
Bibliographic record
Abstract
Abstract Human evolution involves population splits, size fluctuations, founder effects, and admixture. Population history reconstruction based on genetic diversity data routinely relies on simple demographic models while projecting the past. No specific demographic assumptions are needed to understand the genetic structure of the founder population of Québec. Because genealogy and genetics are intimately related, we used descending genealogies of this population to pursue the fate of its founder lineages. Maternal and paternal lines reflect the transmission of mtDNA and the Y-chromosome, respectively. We followed their transmission in real-time, from the 17 th century down to its 20 th -century population. We counted the number of married children of immigrants (i.e., their effective family size, EFS), estimated the proportion of successful immigrants in terms of their survival ratio, and assessed net growth rates and extinction. Likewise, we evaluated the same parameters for their Québec-born descendants. The survival ratio of the first immigrants was the highest and declined over time in association with the decreasing immigrants’ EFS. Parents with high EFS left plentiful married progeny, putting EFS as the most important variable determining the parental demographic success throughout time for generations ahead. The 17 th and 18 th -century immigrants bear the most remarkable demographic and genetic impact on the 20 th -century population of Québec. Lessons learned from Québec genealogies can teach us about the consequences of founder effects and migrations through real people’s history. The effective family size of immigrant founders predicts their long-term demographic outcome.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".