Curated mitochondrial genome reference database of state key protected wild mammal in China
Bibliographic record
Abstract
Effective conservation of wild mammals necessitates accurate taxonomic classification and reliable genetic reference data. In China, the List of State Key Protected Wild Animals serves as a critical tool for species protection. However, taxonomic revisions and gaps in genetic data can impede its effectiveness. In this study, we updated the List of State Key Protected Wild Animals (2021) by incorporating recent taxonomic and distributional evidence, resulting in a refined list of 169 mammalian species that are protected. We identified 15 taxa lacking complete mitochondrial genome data and addressed this gap by generating 12 new mitogenomes for nine taxa using a combination of GenBank database mining and next-generation sequencing of museum specimens and fecal samples. These efforts led to the establishment of a curated mitochondrial genome reference database encompassing 164 species. Our analyses also uncovered taxonomic ambiguities in genera such as Moschus and Naemorhedus, and highlighted mislabeling issues within public genetic databases. This curated database enhances the accuracy of forensic species identification, supports biodiversity monitoring, and strengthens wildlife law enforcement. Our findings underscore the value of integrating historical specimens with mitogenomic approaches to advance wildlife conservation efforts.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".