Sequences Comparison and Phylogenetic Tree Analysis of 288 Strains of Porcine Circovirus
Bibliographic record
Abstract
In order to know the heredity disciplinarian of porcine circovirus(pcv) and pave the basement for preventing and controlling the disease.In this research,nucleotide sequences of 26 strains of PCV1 and 262 strains of PCV2 were analyzed,also,the ORF1's and ORF2's nucleotide sequences and deduced amino acid sequences were analyzed.At the nucleotide level,the results show that PCV1sequences and PCV2 sequences were divided into two different branches,and among the PCV1 sequences there were no obvious regularity,but among PCV2 sequences,there were divided into two different sub-gene types.The sub-gene typeⅠwas mainly consisted of sequences from American,Canada and Australia,and the sub-gene typeⅡwas mainly consisted of sequences from Franch and Newzealand.At the amino acid level,the phylogenetic tree was very complexity,and there were no obvious regularity.It was found statistically that,although nearly ten years pasted, the sequences homology of PCV had no large changes,the results showed that PCV was very conserved.After analyzed the phylogenetic tree,we discovered that the distributing of PCV had no geographic and time restriction.Also,we analyzed the mutant dots of the PCV2 nucleotide sequences and deduced amino acid sequences,we discovered that,PCV2 nucleotide sequences were easy to has base transversion.
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 | 0.000 | 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) | 0.001 | 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".