A Tale of Immigration Told by Geneaology
Bibliographic record
Abstract
A 23andMe DNA ancestry composition test was taken to derive specific genetic information such as my maternal mitochondrial DNA (maternal haplogroup) and my ancestral DNA composition from different regions of the world based on similarities to other individuals in those regions with the same genotype patterns. Deep historical ancestry showed my maternal haplogroup as U5a1b. This indicated a maternal heritage line migrating north through Africa and up into Western Europe around 47,000 years ago. My ancestral DNA composition showed heritage from the following regions: French & German (Germany) 84.9%, Eastern European (Russia/Lithuania) 8.7%, and Southern European (Italy) 3.1%. The German and Italian results were expected however the Russian/Lithuiana heritage was originally hypothesized from my paternal grandfather due to a past ancestor's Germany to Russia immigration. However this has been dismissed as socially unacceptable/unexpected in that period, so the Eastern European heritage remains a mystery in origin. Verbal and document-supported accounts from ancestors and family members explain the various immigrations conducted through my maternal/Humer family and my paternal/Wirtz family. Specifically, my maternal grandparents and their separate immigrations in the 1950s to southern Ontario and my separate paternal great-grandparents immigration from Germany to Russia to Port Huron, Michigan.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.023 |
| Scholarly communication | 0.005 | 0.015 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.015 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".