When Genetics and Genealogies Tell Different Stories-Maternal Lineages in Gaspesia
Bibliographic record
Abstract
Data from uniparentally inherited genetic systems were used to trace evolution of human populations. Reconstruction of the past primarily relies on variation in present-day populations, limiting historical inference to lineages that are found among living subjects. Our analysis of four population groups in the Gaspé Peninsula, demonstrates how this may occasionally lead to erroneous interpretations. Mitochondrial DNA analysis of Gaspesians revealed an important admixture with Native Americans. The most likely scenario links this admixture to French-Canadians from the St. Lawrence Valley who moved to Gaspesia in the 19th century. However, in contrast to genetic data, analysis of genealogical record shows that Native American maternal lineages were brought to Gaspesia in the 18th century by Acadians who settled on the south-western coast of the peninsula. Intriguingly, within three generations, virtually all Métis Acadian families separated from their nonadmixed relatives and moved eastward mixing in with other Gaspesian groups, in which Native American maternal lines are present in relatively high frequencies. Over time, the carriers of these lines eventually lost memory of their mixed Amerindian-Acadian origin. Our results show that a reliable reconstruction of population history requires cross-verification of different data sources for consistency, thus favouring multidisciplinary approaches.
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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 | 0.002 | 0.009 |
| 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 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".