Century-old ethanol-preserved Vega Collection reveal the unexpected phylogeography of Slender bitterling <i>Tanakia lanceolata</i> in Japan
Bibliographic record
Abstract
Abstract Recent genetic advancements offer new opportunities for studying natural history museum collections. The genetic analysis of century-old aquatic animal specimens, mostly preserved in ethanol, can provide valuable insights into the changes in genetic diversity caused by anthropogenic impacts. However, knowledge of the characteristics of degraded DNA from such specimens remains limited. In this study, we evaluated the DNA quality of bitterling fish, Tanakia lanceolata (Temminck and Schlegel), collected during the Vega Expedition in Japan in 1879 and preserved in ethanol. We then performed genomic analysis to test its hypothesized translocation-involved population history. The historical DNA was degraded, peaking around 50 bp, but a minor fraction exceeded 300 bp. Mitochondrial genetic analysis revealed high genetic similarity between the eastern and western sides of the Central Highland, typically impeding the dispersal of primary freshwater fish. These findings suggest an unexpected dispersal ability in T. lanceolata or undocumented translocations before the large-scale domestic translocations recorded since the 1910s. The customized workflow for historical ethanol-preserved specimens, based on historical DNA quality in this study, provides a foundation for studying historical archives.
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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 | 0.000 | 0.000 |
| 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".