Genetic resiliency and the Black Death: No apparent loss of mitogenomic diversity due to the Black Death in medieval London and Denmark
Bibliographic record
Abstract
Abstract Objectives In the 14th century AD, medieval Europe was severely affected by the Great European Famine as well as repeated bouts of disease, including the Black Death, causing major demographic shifts. This high volatility led to increased mobility and migration due to new labor and economic opportunities, as evidenced by documentary and stable isotope data. This study uses ancient DNA (aDNA) isolated from skeletal remains to examine whether evidence for large‐scale population movement can be gleaned from the complete mitochondrial genomes of 264 medieval individuals from England (London) and Denmark. Materials and Methods Using a novel library‐conserving approach to targeted capture, we recovered 264 full mitochondrial genomes from the petrous portion of the temporal bones and teeth and compared genetic diversity across the medieval period within and between English (London) and Danish populations and with contemporary populations through population pairwise ΦSTanalysis. Results We find no evidence of significant differences in genetic diversity spatially or temporally in our dataset, yet there is a high degree of haplotype diversity in our medieval samples with little exact sequence sharing. Discussion The mitochondrial genomes of both medieval Londoners and medieval Danes suggest high mitochondrial diversity before, during and after the Black Death. While our mitochondrial genomic data lack geographically correlated signals, these data could be the result of high, continual female migration before and after the Black Death or may simply indicate a large female effective population size unaffected by the upheaval of the medieval period. Either scenario suggests a genetic resiliency in areas of northwestern medieval Europe.
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 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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".