The origin of the Tibetan Mastiff and species identification of Canis based on mitochondrial cytochrome c oxidase subunit I (COI) gene and COI barcoding
Bibliographic record
Abstract
DNA barcoding is an effective technique to identify species and analyze phylogenesis and evolution. However, research on and application of DNA barcoding in Canis have not been carried out. In this study, we analyzed two species of Canis, Canis lupus (n = 115) and Canis latrans (n = 4), using the cytochrome c oxidase subunit I (COI) gene (1545 bp) and COI barcoding (648 bp DNA sequence of the COI gene). The results showed that the COI gene, as the moderate variant sequence, applied to the analysis of the phylogenesis of Canis members, and COI barcoding applied to species identification of Canis members. Phylogenetic trees and networks showed that domestic dogs had four maternal origins (A to D) and that the Tibetan Mastiff originated from Clade A; this result supports the theory of an East Asian origin of domestic dogs. Clustering analysis and networking revealed the presence of a closer relative between the Tibetan Mastiff and the Old English sheepdog, Newfoundland, Rottweiler and Saint Bernard, which confirms that many well-known large breed dogs in the world, such as the Old English sheepdog, may have the same blood lineage as that of the Tibetan Mastiff.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".