Completely Sequencing and Gene Organization of the Anser cygnoides Mitochondrial Genome
Bibliographic record
Abstract
Mitochondrial genome(mt DNA) has advantages in rapid evolution, rich polymorphism and maternally inheritance without gene recombinations, which has become an ideal molecular markers of population genetics, phylogenetics, molecular ecology and taxonomy. In this study, the primers were designed based on the mitochondrial genome sequence of Bean goose(Anser fabalis) which was a closely related species of Swan goose(Anser cygnoides). Swan goose mitochondrial genome sequence was analysed by direct sequencing techniques. The results showed that whole mitochondrial genome sequence was 16 739 bp(Gen Bank accession No. KJ124555) in Swan goose, including 22 t RNA genes, 2 r RNA genes, 13 proteincoding genes and a D-loop region. Base composition of T, C, A and G were 22.49%, 32.24%, 30.21% and15.06%, respectively. Besides, the base preference of AT was not determined. 22 kinds of t RNA were all typically cloverleaf structures. Compared to 12 Sr RNA of Red Junglefowl(Gallus gallus) and Mongolian Ground Jay(Podoces hendersoni), we found the secondary structure of 12 Sr RNA included 4 domains, 37 stemloops and 13 salients in the Swan goose, and LSP/HSP, ETAS1- 2, goose hairpin, E- box, F- box, D- box, Cbox, Bird similarity- box, CSB1- box, CSB- like and OH in the D- loop control region. Finally, taken Red Junglefowl as an outgroup, the phylogenetic tree was constructed based on mitochondrial genome sequences using Neighbor-joining(N-J) algorithm, Maxium-likelihood(ML) algorithm and Bayesian model. The results showed that Swan goose, Greylag goose, Bean goose, white- fronted goose and Canada goose had close genetic relationship. The findings enrich the ducks mitochondrial genome sequences and provide a theoretical basis for the study of geese phylogeny.
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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 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".