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Record W7024897670

A Tale of Immigration Told by Geneaology

2024· article· en· W7024897670 on OpenAlexaboutno aff

Bibliographic record

VenueNSUWorks (Nova Southeastern University) · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicForensic and Genetic Research
Canadian institutionsnot available
Fundersnot available
KeywordsImmigrationGrandparentGermanHaplogroupWestern europeGenetic genealogyAncient DNAHaplotypeEthnic compositionNorwegian
DOInot available

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.701
Threshold uncertainty score0.406

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.009
GPT teacher head0.233
Teacher spread0.223 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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