Global population divergence and admixture of the brown rat ( <i>Rattus norvegicus</i> )
Bibliographic record
Abstract
Native to China and Mongolia, the brown rat (Rattus norvegicus) now enjoys a worldwide distribution. While black rats and the house mouse tracked the regional development of human agricultural settlements, brown rats did not appear in Europe until the 1500s, suggesting their range expansion was a response to relatively recent increases in global trade. We inferred the global phylogeography of brown rats using 32 k SNPs, and detected 13 evolutionary clusters within five expansion routes. One cluster arose following a southward expansion into Southeast Asia. Three additional clusters arose from two independent eastward expansions: one expansion from Russia to the Aleutian Archipelago, and a second to western North America. Westward expansion resulted in the colonization of Europe from which subsequent rapid colonization of Africa, the Americas and Australasia occurred, and multiple evolutionary clusters were detected. An astonishing degree of fine-grained clustering between and within sampling sites underscored the extent to which urban heterogeneity shaped genetic structure of commensal rodents. Surprisingly, few individuals were recent migrants, suggesting that recruitment into established populations is limited. Understanding the global population structure of R. norvegicus offers novel perspectives on the forces driving the spread of zoonotic disease, and aids in development of rat eradication programmes.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".