Bibliographic record
Abstract
The population of Quebec, Canada (7.3 million) contains approximately 6 million French Canadians; they are the descendants of approximately 8500 permanent French settlers who colonized Nouvelle France between 1608 and 1759. Their well-documented settlements, internal migrations, and natural increase over four centuries in relative isolation (geographic, linguistic, etc.) contain important evidence of social transmission of demographic behavior that contributed to effective family size and population structure. This history is reflected in at least 22 Mendelian diseases, occurring at unusually high prevalence in its subpopulations. Immigration of non-French persons during the past 250 years has given the Quebec population further inhomogeneity, which is apparent in allelic diversity at various loci. The histories of Quebec's subpopulations are, to a great extent, the histories of their alleles. Rare pathogenic alleles with high penetrance and associated haplotypes at 10 loci (CFTR, FAH, HBB, HEXA, LDLR, LPL, PAH, PABP2, PDDR, and SACS) are expressed in probands with cystic fibrosis, tyrosinemia, beta-thalassemia, Tay-Sachs, familial hypercholesterolemia, hyperchylomicronemia, PKU, oculopharyngeal muscular dystrophy, pseudo vitamin D deficiency rickets, and spastic ataxia of Charlevoix-Saguenay, respectively) reveal the interpopulation and intrapopulation genetic diversity of Quebec. Inbreeding does not explain the clustering and prevalence of these genetic diseases; genealogical reconstructions buttressed by molecular evidence point to founder effects and genetic drift in multiple instances. Genealogical estimates of historical meioses and analysis of linkage disequilibrium show that sectors of this young population are suitable for linkage disequilibrium mapping of rare alleles. How the population benefits from what is being learned about its structure and how its uniqueness could facilitate construction of a genomic map of linkage disequilibrium are discussed.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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.
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".